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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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33 pages, 16520 KiB  
Article
Enhanced Non-Destructive Testing of Small Wind Turbine Blades Using Infrared Thermography
by Majid Memari, Mohammad Shekaramiz, Mohammad A. S. Masoum and Abdennour C. Seibi
Machines 2025, 13(2), 108; https://doi.org/10.3390/machines13020108 - 29 Jan 2025
Viewed by 647
Abstract
This study presents a foundational step in a broader initiative aimed at leveraging thermal imaging technology to enhance wind turbine maintenance, particularly focusing on the challenges of detecting defects and object localization in small wind turbine blades. Serving as a preliminary experiment, this [...] Read more.
This study presents a foundational step in a broader initiative aimed at leveraging thermal imaging technology to enhance wind turbine maintenance, particularly focusing on the challenges of detecting defects and object localization in small wind turbine blades. Serving as a preliminary experiment, this research project tested methodologies and technologies on a smaller scale before advancing to more complex applications involving large, operational wind turbines using drone-mounted cameras. Utilizing thermal cameras suitable for both handheld and drone use, alongside advanced image processing applications, we navigated the significant challenge of acquiring high-quality thermal images to detect small defects. This required a concentrated analysis of a select subset of data and a methodological shift towards object detection and localization using the You Only Look Once (YOLO) model versions 8 and 9. This effort not only paves the way for applying these techniques to larger-scale turbines but also contributes to the ongoing development of an integrated maintenance strategy in the wind energy sector. Highlighting the critical impact of environmental conditions on thermal imaging, our research underscores the importance of continued exploration in this field, especially in enhancing object localization techniques for the future drone-based maintenance of operational wind turbine blades (WTBs). Full article
(This article belongs to the Special Issue Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis)
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20 pages, 11567 KiB  
Article
Experimental Acoustic Noise and Sound Quality Characterization of a Switched Reluctance Motor Drive with Hysteresis and PWM Current Control
by Moien Masoumi and Berker Bilgin
Machines 2025, 13(2), 82; https://doi.org/10.3390/machines13020082 - 23 Jan 2025
Viewed by 548
Abstract
This paper presents an experimental characterization of acoustic noise and sound quality in a 12/8 Switched Reluctance Motor (SRM) using hysteresis and Pulse Width Modulation (PWM) current control techniques. To overcome the limitations of traditional sound power measurements and enhance the accuracy of [...] Read more.
This paper presents an experimental characterization of acoustic noise and sound quality in a 12/8 Switched Reluctance Motor (SRM) using hysteresis and Pulse Width Modulation (PWM) current control techniques. To overcome the limitations of traditional sound power measurements and enhance the accuracy of acoustic noise evaluation, a setup is applied for calculating sound power based on sound intensity measurements. The study provides a detailed description of the intensity probe-holding fixture, the hardware configuration for acoustic noise experiments, and the software setup tailored to specific measurement requirements. The acoustic noise characteristics of the motor are assessed at various operating points using two distinct current control methods: hysteresis current control with a variable switching frequency of up to 20 kHz and PWM current control with a fixed switching frequency of 12.5 kHz. Measurements of sound pressure and sound intensity enable the calculation of sound power and sound quality metrics under different operating conditions. Furthermore, the study investigates the influence of various factors on the motor’s sound power levels and sound quality. The findings provide valuable insights into the contributions of these factors to acoustic noise characteristics and offer a foundation for improving the motor’s acoustic behavior during the design and control stages. Full article
(This article belongs to the Special Issue Advances in Noises and Vibrations for Machines)
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27 pages, 14486 KiB  
Article
Hollow Direct Air-Cooled Rotor Windings: Conjugate Heat Transfer Analysis
by Avo Reinap, Samuel Estenlund and Conny Högmark
Machines 2025, 13(2), 89; https://doi.org/10.3390/machines13020089 - 23 Jan 2025
Viewed by 508
Abstract
This article focuses on the analysis of a direct air-cooled rotor winding of a wound field synchronous machine, the innovation of which lies in the increase in the internal cooling surface, the cooling of the winding compared to the conventional inter-pole cooling, and [...] Read more.
This article focuses on the analysis of a direct air-cooled rotor winding of a wound field synchronous machine, the innovation of which lies in the increase in the internal cooling surface, the cooling of the winding compared to the conventional inter-pole cooling, and the development of a CHT evaluation model accordingly. Conjugate heat transfer (CHT) analysis is used to explore the cooling efficacy of a parallel-cooled hollow-conductor winding of a salient-pole rotor and to identify a cooling performance map. The use of high current densities of 15–20 Arms/mm2 in directly cooled windings requires high cooling intensity, which in the case of air cooling results not only in flow velocities above 15 m/s to ensure permissible operating temperatures, but also the need for coolant distribution and heat transfer studies. The experiments and calculations are based on a non-rotating machine and a wind tunnel using the same rotor coil(s). CHT-based thermal calculations provide not only reliable results compared to experimental work and lumped parameter thermal circuits with adjusted aggregate parameters, but also insight related to pressure and cooling flow distribution, thermal loads, and cooling integration issues that are necessary for the development of high power density and reliable electrical machines. The results of the air-cooling integration show that the desired high current density is achievable at the expense of high cooling intensity, where the air velocity ranges from 15 to 30 m/s and 30 to 55 m/s, distinguishing the air velocity of the hollow conductor and bypass channel, compared to the same coil in an electric machine and a wind tunnel at the similar thermal load and limit. Since the hot spot location depends on cooling integration and cooling intensity, modeling and estimating the cooling flow is essential in the development of wound-field synchronous machines. Full article
(This article belongs to the Section Electrical Machines and Drives)
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27 pages, 18817 KiB  
Article
Research on Bolt Loosening Mechanism Under Sine-on-Random Coupling Vibration Excitation
by Jiangong Du, Yuanying Qiu and Jing Li
Machines 2025, 13(2), 80; https://doi.org/10.3390/machines13020080 - 23 Jan 2025
Viewed by 569
Abstract
This paper primarily investigates the mechanism of bolt loosening under the Sine-on-Random (SOR) vibration excitation. Firstly, a theoretical model of bolt loosening response under the SOR synthesized excitation is established by a time–frequency conversion method, which converts the sine excitation into Power Spectrum [...] Read more.
This paper primarily investigates the mechanism of bolt loosening under the Sine-on-Random (SOR) vibration excitation. Firstly, a theoretical model of bolt loosening response under the SOR synthesized excitation is established by a time–frequency conversion method, which converts the sine excitation into Power Spectrum Density (PSD) expression in the frequency domain and superimposes it with random vibration excitation to obtain the SOR synthesized excitation spectrum. Then, by means of a four-bolt fastened structure, the bolt loosening mechanisms under both the sine and random vibration excitation are deeply studied, respectively. Ultimately, based on the time–frequency conversion method of SOR synthesized excitation, the bolt loosening responses of the structure under SOR excitation with different tightening torques are analyzed. Furthermore, a three-stage criterion including the Steady Stage, Transition Stage, and Loosen Stage for bolt loosening under SOR excitation is revealed, and the relationship among the SOR synthesized vibration responses and the two forms of single vibration responses is explored based on a corrective energy superposition method by introducing the weight factors of the two single vibration responses under different tightening torques. Finally, test verifications for the four-bolt fastened structure are conducted and good consistencies with the results of the Finite Element Analysis (FEA) are shown. This study provides valuable insights into the detection and prevention of loosening in bolted connection structures under multi-source vibration environments and has important engineering reference significance. Full article
(This article belongs to the Special Issue Design and Experimental Activity of Testing Machines and Mechanical Test Rigs)
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25 pages, 24599 KiB  
Article
MCBA-MVACGAN: A Novel Fault Diagnosis Method for Rotating Machinery Under Small Sample Conditions
by Wenhan Huang, Xiangfeng Zhang, Hong Jiang, Zhenfa Shao and Yu Bai
Machines 2025, 13(1), 71; https://doi.org/10.3390/machines13010071 - 20 Jan 2025
Viewed by 631
Abstract
In complex industrial scenarios, high-quality fault data of rotating machinery are scarce and costly to collect. Therefore, small sample fault diagnosis needs further research. To solve this problem, in this work is proposed a minimum variance auxiliary classifier generation adversarial network based on [...] Read more.
In complex industrial scenarios, high-quality fault data of rotating machinery are scarce and costly to collect. Therefore, small sample fault diagnosis needs further research. To solve this problem, in this work is proposed a minimum variance auxiliary classifier generation adversarial network based on a multi-scale convolutional block attention mechanism. Firstly, the multi-scale convolutional block attention mechanism is designed to extract multi-scale information and perform weighted fusion to enhance the ability of the model to capture effective features. Secondly, the minimum variance term is designed to minimize the variance of sample distribution, so that the generated samples are distributed more evenly in the feature space, avoiding the problem of pattern collapse. Finally, the objective function is reconstructed by independent classification loss to improve the ability of model data generation. Experimental results on CWRU and gearbox datasets validate the effectiveness and reliability of the proposed method. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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19 pages, 9423 KiB  
Article
A Common DC Bus Circulating Current Suppression Method for Motor Emulators of New Energy Vehicles
by Haonan Sun, Dafang Wang, Qi Li and Yingkang Qin
Machines 2025, 13(1), 51; https://doi.org/10.3390/machines13010051 - 13 Jan 2025
Viewed by 544
Abstract
In contrast to the conventional topology, wherein the Device Under Test (DUT) controller and the electric motor emulator (EME) are powered by the DC (Direct Current) voltage source independently, the common DC bus topology necessitates a single power supply. This reduces the cost [...] Read more.
In contrast to the conventional topology, wherein the Device Under Test (DUT) controller and the electric motor emulator (EME) are powered by the DC (Direct Current) voltage source independently, the common DC bus topology necessitates a single power supply. This reduces the cost and complexity of the motor emulator system, making it more favorable for large-scale industrial applications. However, this topology introduces significant circulating current issues in the system. A common DC bus circulating current suppression method is proposed in this paper for the motor emulator. First, the mechanism of zero-sequence circulating current generation in the common DC bus topology is analyzed and the expression for the system’s zero-sequence voltage difference is derived. Then, a control method based on a Hybrid PWM (Pulse Width Modulation) strategy that unifies SPWM (SIN Pulse Width Modulation) and SVPWM (Space Vector Pulse Width Modulation) is proposed, which has been shown to be effective in suppressing the zero-sequence circulating current in a motor emulator system with a common DC bus topology. The proposed control method has been experimentally validated using a motor emulator system. Full article
(This article belongs to the Special Issue New Trends in Reliability and Lifetime Improvement in Power-Electronic-Controlled Machines and Devices)
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31 pages, 21587 KiB  
Article
Bearing Fault Feature Extraction Method Based on Adaptive Time-Varying Filtering Empirical Mode Decomposition and Singular Value Decomposition Denoising
by Xuezhuang E, Wenbo Wang and Hao Yuan
Machines 2025, 13(1), 50; https://doi.org/10.3390/machines13010050 - 13 Jan 2025
Viewed by 518
Abstract
Aiming to address the difficulty in extracting the early weak fault features of bearings under complex operating conditions, a fault diagnosis method is proposed based on the adaptive fusion of time-varying filtering empirical mode decomposition (TVF-EMD) modal components and singular value decomposition (SVD) [...] Read more.
Aiming to address the difficulty in extracting the early weak fault features of bearings under complex operating conditions, a fault diagnosis method is proposed based on the adaptive fusion of time-varying filtering empirical mode decomposition (TVF-EMD) modal components and singular value decomposition (SVD) noise reduction. First, the snake optimization (SO) technique is used to optimize the TVF-EMD algorithm in order to determine the optimal parameters that match the input signal. Then, the bearing signal is divided into a number of intrinsic mode functions (IMFs) using TVF-EMD in order to reduce the nonlinearity and non-stationary characteristics of the fault signal. An index for the envelope fault information energy ratio (EFIER) is created to overcome the drawback of there being too many IMF components after TVF-EMD decomposition. The IMF components are ranked in descending order according to the EFIER, and they are fused according to the maximum principle of the energy ratio of envelope fault information until the optimal fusion component is determined. Finally, the fault feature is extracted when the optimal fusion component is denoised using SVD. Two measured bearing fault signals and simulation signals are used to validate the performance of the proposed method. The experimental findings demonstrate that the approach has good sensitive feature screening, fusion, and noise reduction capabilities. The proposed method can more precisely extract the early fault features of bearings and accurately identify fault types. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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15 pages, 6899 KiB  
Article
Influence of Potting Radius on the Structural Performance and Failure Mechanism of Inserts in Sandwich Structures
by Filippos Filippou and Alexis Τ. Kermanidis
Machines 2025, 13(1), 34; https://doi.org/10.3390/machines13010034 - 7 Jan 2025
Viewed by 596
Abstract
In this study, the mechanical performance and failure modes of cold-potted inserts within sandwich structures were examined, focusing on the influence of the potting radius, while maintaining constant insert radius and specimen characteristics. In this research, destructive testing was used to evaluate the [...] Read more.
In this study, the mechanical performance and failure modes of cold-potted inserts within sandwich structures were examined, focusing on the influence of the potting radius, while maintaining constant insert radius and specimen characteristics. In this research, destructive testing was used to evaluate the pull out, load-carrying capacity, and failure mechanisms of the inserts. The methods of stiffness degradation and acoustic emissions (AE) were employed for structural health monitoring to capture real-time data on failure progression, including core buckling, core rupture, and skin delamination. The results indicated that increasing the potting radius significantly altered the failure modes and critical failure load of the insert system. A critical potting radius was identified where maximum stiffness was achieved. Beyond this point, insert fracture became the dominant failure mode, with minimal damage to the surrounding core and CFRP skins. Larger potting radii also led to reduced displacement at failure, increased ultimate loads, and elevated stiffness, which were maintained until sudden structural failure. Through detailed isolation and observation of each failure event and with the use of AE data, precise identification of system damage in real time was allowed, offering insights into the progression and causes of failure. Full article
(This article belongs to the Special Issue State of the Art in the Field of Machine and System Testing to Assist in the Diagnosis and Prognosis of Failures)
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18 pages, 4724 KiB  
Article
A Wearable Stiffness-Rendering Haptic Device with a Honeycomb Jamming Mechanism for Bilateral Teleoperation
by Thomas M. Kwok, Bohan Zhang and Wai Tuck Chow
Machines 2025, 13(1), 27; https://doi.org/10.3390/machines13010027 - 6 Jan 2025
Viewed by 840
Abstract
This paper addresses the challenge of providing kinesthetic feedback in bilateral teleoperation by designing a wearable, lightweight (20 g), and compact haptic device, the HJ-Haptic, utilizing a honeycomb jamming mechanism for object stiffness rendering. The HJ-Haptic device can vary its stiffness, from 1.15 [...] Read more.
This paper addresses the challenge of providing kinesthetic feedback in bilateral teleoperation by designing a wearable, lightweight (20 g), and compact haptic device, the HJ-Haptic, utilizing a honeycomb jamming mechanism for object stiffness rendering. The HJ-Haptic device can vary its stiffness, from 1.15 N/mm to 2.64 N/mm, using a 30 kPa vacuum pressure. We demonstrate its implementation in a teleoperation framework, enabling operators to adjust grip force based on a reliable haptic feedback on object stiffness. A three-point flexural test on the honeycomb jamming mechanism and teleoperated object-grasping tasks were conducted to evaluate the device’s functionality. Our experiments demonstrated a small RMSE and strong correlations in teleoperated motion, stiffness rendering, and interaction force feedback. The HJ-Haptic effectively adjusts its stiffness in response to real-time gripper feedback, mimicking the sensation of direct object grasping with hands. The device’s use of vacuum pressure ensures operator safety by preventing dangerous outcomes in case of gas leakage or material failure. Incorporating the HJ-Haptic into the teleoperation framework provided the reliable perception of object stiffness and stable teleoperation. This study highlights the potential of the honeycomb jamming mechanism for enhancing haptic feedback in various applications, including teleoperation scenarios, as well as interactions with extended-reality environments. Full article
(This article belongs to the Special Issue Advances and Challenges in Wearable Robotics)
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29 pages, 6463 KiB  
Article
A Novel FS-GAN-Based Anomaly Detection Approach for Smart Manufacturing
by Tae-yong Kim, Jieun Lee, Seokhyun Gong, Jaehoon Lim, Dowan Kim and Jongpil Jeong
Machines 2025, 13(1), 21; https://doi.org/10.3390/machines13010021 - 31 Dec 2024
Cited by 1 | Viewed by 831
Abstract
In this study, we present the few-shot generative adversarial network (FS-GAN) model, which integrates few-shot learning and a generative adversarial network with an unsupervised learning approach (AnoGAN) to address the challenges of anomaly detection in smart-factory manufacturing environments. Manufacturing processes often encounter malfunctions [...] Read more.
In this study, we present the few-shot generative adversarial network (FS-GAN) model, which integrates few-shot learning and a generative adversarial network with an unsupervised learning approach (AnoGAN) to address the challenges of anomaly detection in smart-factory manufacturing environments. Manufacturing processes often encounter malfunctions or defective parts that disrupt production and compromise product quality. However, collecting and labeling sufficient data to detect anomalies is time-intensive, and abnormal data are rare, leading to data imbalances. The FS-GAN model leverages few-shot learning to enable accurate predictions with minimal data and uses the generative capabilities of AnoGAN to mitigate the scarcity of abnormal data by generating synthetic normal data. Experimental results demonstrate that FS-GAN outperforms existing models in terms of accuracy and learning speed, even with limited datasets, effectively addressing the data imbalance problem inherent in manufacturing. The model reduces dependency on extensive data collection and labeling efforts, making it suitable for real-world applications. Through reliable and efficient anomaly detection, FS-GAN contributes to production reliability, product quality, and operational efficiency in smart factories. This study highlights the potential of FS-GAN to provide a cost-effective and high-performance solution to the challenges of anomaly detection in the manufacturing industry. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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20 pages, 816 KiB  
Article
Condition Monitoring of Marine Diesel Lubrication System Based on an Optimized Random Singular Value Decomposition Model
by Shuxia Ye, Bin Da, Liang Qi, Han Xiao and Shankai Li
Machines 2025, 13(1), 7; https://doi.org/10.3390/machines13010007 - 25 Dec 2024
Viewed by 609
Abstract
As modern marine diesel engine systems become increasingly complex, effective condition monitoring methods are essential for ensuring optimal performance and preventing anomalies. This paper proposes a data-driven condition monitoring approach specifically designed for the lubrication system of marine diesel engines. Unlike traditional methods, [...] Read more.
As modern marine diesel engine systems become increasingly complex, effective condition monitoring methods are essential for ensuring optimal performance and preventing anomalies. This paper proposes a data-driven condition monitoring approach specifically designed for the lubrication system of marine diesel engines. Unlike traditional methods, the proposed approach eliminates the need for explicit modeling and leverages a novel optimization algorithm for data denoising. Additionally, a new noise-resistant monitoring index is introduced to enhance monitoring reliability. The paper is structured into two main sections for validation. The first section addresses advanced data preprocessing, where the Improved Sparrow Search Algorithm (ISSA) is employed to optimize the parameters of Random Singular Value Decomposition (RSVD). This step effectively minimizes noise, reduces manual intervention, and handles high-dimensional data. The second section focuses on analyzing the data characteristics using the Random Matrix Theory (RMT) and establishing novel condition monitoring indicators to achieve more reliable monitoring outcomes. The proposed methodology captures the intricate relationships among key variables within the system, providing a more robust framework for condition monitoring. Applied to a marine diesel engine lubrication system, the method demonstrates significant improvements in noise immunity and monitoring reliability. Comparative analyses of condition monitoring models before and after denoising reveal that the relative error of the proposed monitoring index under varying noise amplitudes is within 1%, substantially lower than that of other indices. Furthermore, the monitoring accuracy is improved by 4.95% when the proposed index is employed for system condition monitoring. Full article
(This article belongs to the Special Issue Advanced Techniques for Fault Detection, Diagnosis, and Prognostics in Machinery)
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25 pages, 1305 KiB  
Article
Transitioning from Simulation to Reality: Applying Chatter Detection Models to Real-World Machining Data
by Matthew Alberts, Sam St. John, Simon Odie, Anahita Khojandi, Bradley Jared, Tony Schmitz, Jaydeep Karandikar and Jamie B. Coble
Machines 2024, 12(12), 923; https://doi.org/10.3390/machines12120923 - 17 Dec 2024
Cited by 2 | Viewed by 777
Abstract
Chatter, a self-excited vibration phenomenon, is a critical challenge in high-speed machining operations, affecting tool life, product surface quality, and overall process efficiency. While machine learning models trained on simulated data have shown promise in detecting chatter, their real-world applicability remains uncertain due [...] Read more.
Chatter, a self-excited vibration phenomenon, is a critical challenge in high-speed machining operations, affecting tool life, product surface quality, and overall process efficiency. While machine learning models trained on simulated data have shown promise in detecting chatter, their real-world applicability remains uncertain due to discrepancies between simulated and actual machining environments. The primary goal of this study is to bridge the gap between simulation-based machine learning models and real-world applications by developing and validating a Random Forest-based chatter detection system. This research focuses on improving manufacturing efficiency through reliable chatter detection by integrating Operational Modal Analysis (OMA), Receptance Coupling Substructure Analysis (RCSA), and Transfer Learning (TL). The study applies a Random Forest classification model trained on over 140,000 simulated machining datasets, incorporating techniques like Operational Modal Analysis (OMA), Receptance Coupling Substructure Analysis (RCSA), and Transfer Learning (TL) to adapt the model for real-world operational data. The model is validated against 1600 real-world machining datasets, achieving an accuracy of 86.1%, with strong precision and recall scores. The results demonstrate the model’s robustness and potential for practical implementation in industrial settings, highlighting challenges such as sensor noise and variability in machining conditions. This work advances the use of predictive analytics in machining processes, offering a data-driven solution to improve manufacturing efficiency through more reliable chatter detection. Full article
(This article belongs to the Special Issue Application of Sensing Measurement in Machining)
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15 pages, 3412 KiB  
Article
Prediction of Fretting Wear Lifetime of a Coated System
by Kyungmok Kim
Machines 2024, 12(12), 910; https://doi.org/10.3390/machines12120910 - 11 Dec 2024
Viewed by 599
Abstract
This article proposes a model of predicting the fretting wear lifetime of a low-friction coating. The proposed model incorporates multiple factors that influence the fretting wear damage of coatings: the imposed contact load, imposed average velocity, coating hardness, and initial surface roughness of [...] Read more.
This article proposes a model of predicting the fretting wear lifetime of a low-friction coating. The proposed model incorporates multiple factors that influence the fretting wear damage of coatings: the imposed contact load, imposed average velocity, coating hardness, and initial surface roughness of counterparts. The fretting wear lifetime of coatings, defined as the number of cycles critical to friction coefficient evolution, was collected from the literature. For the purpose of identifying parameters in the model, experimental fretting wear lifetime data were analyzed. The results show that the fretting wear lifetime of a coating can be described by an inverse power law regarding the contact load, imposed average velocity, and initial surface roughness of counterparts. In contrast, the fretting wear lifetime of a coating was observed to increase with increased coating hardness. It was observed that the exponents of the inverse power law varied with respect to the type of coating. The proposed fretting wear lifetime model enables the prediction of coating lifetime under various fretting conditions. Full article
(This article belongs to the Special Issue Design and Characterization of Engineered Bearing Surfaces)
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23 pages, 5495 KiB  
Article
Optimization of Machining Parameters for Reducing Drum Shape Error Phenomenon in Wire Electrical Discharge Machining Processes
by Shih-Ming Wang, Li-Jen Hsu, Hariyanto Gunawan and Ren-Qi Tu
Machines 2024, 12(12), 908; https://doi.org/10.3390/machines12120908 - 10 Dec 2024
Viewed by 627
Abstract
Machining thicker workpieces in the process of Wire Electrical Discharge Machining (WEDM) can result in a concave phenomenon known as a “drum shape error” due to the vibration of wires and accumulation of debris, which leads to secondary discharge in the middle of [...] Read more.
Machining thicker workpieces in the process of Wire Electrical Discharge Machining (WEDM) can result in a concave phenomenon known as a “drum shape error” due to the vibration of wires and accumulation of debris, which leads to secondary discharge in the middle of the workpiece. Reducing the drum shape error typically requires a longer finishing process. Finding a balance between precision and machining time efficiency has become a challenge for modern machining shops. This study employed experimental analysis to investigate the effect of individual parameters on the shape error and machining removal rate (MRR). Key influential parameters, including open voltage (OV), pulse ON time (ON), pulse OFF time (OFF), and servo voltage (SV), were chosen for data collection using full factorial and Taguchi orthogonal arrays. Regression analysis was conducted to establish multiple regression equations. These equations were used to develop optimization rules, and subsequently, a user-friendly human–machine interface was developed using C# based on these optimization rules to create a shape error and MRR optimization system. The system can predict the optimal parameter combinations to minimize the shape error and increase the MRR. The results of the verification experiments showed that the prediction accuracy can reach 94.7% for shape error and 99.2% for MRR. Additionally, the shape error can be minimized by up to 40%. Full article
(This article belongs to the Special Issue Advances in Noises and Vibrations for Machines)
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18 pages, 10226 KiB  
Article
Hybrid Deep Learning Model for Fault Diagnosis in Centrifugal Pumps: A Comparative Study of VGG16, ResNet50, and Wavelet Coherence Analysis
by Wasim Zaman, Muhammad Farooq Siddique, Saif Ullah, Faisal Saleem and Jong-Myon Kim
Machines 2024, 12(12), 905; https://doi.org/10.3390/machines12120905 - 10 Dec 2024
Viewed by 912
Abstract
Significant in various industrial applications, centrifugal pumps (CPs) play an important role in ensuring operational efficiency, yet they are susceptible to faults that can disrupt production and increase maintenance costs. This study proposes a robust hybrid model for accurate fault detection and classification [...] Read more.
Significant in various industrial applications, centrifugal pumps (CPs) play an important role in ensuring operational efficiency, yet they are susceptible to faults that can disrupt production and increase maintenance costs. This study proposes a robust hybrid model for accurate fault detection and classification in CPs, integrating Wavelet Coherence Analysis (WCA) with deep learning architectures VGG16 and ResNet50. WCA is initially applied to vibration signals, creating time–frequency representations that capture both temporal and frequency information, essential for identifying subtle fault characteristics. These enhanced signals are processed by VGG16 and ResNet50, each contributing unique and complementary features that enhance feature representation. The hybrid approach fuses the extracted features, resulting in a more discriminative feature set that optimizes class separation. The proposed model achieved a test accuracy of 96.39%, demonstrating minimal class overlap in t-SNE plots and a precise confusion matrix. When compared to the ResNet50-based and VGG16-based models from previous studies, which reached 91.57% and 92.77% accuracy, respectively, the hybrid model displayed better classification performance, particularly in distinguishing closely related fault classes. High F1-scores across all fault categories further validate its effectiveness. This work underscores the value of combining multiple CNN architectures with advanced signal processing for reliable fault diagnosis, improving accuracy in real-world CP applications. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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17 pages, 9200 KiB  
Article
Multi-Condition Intelligent Fault Diagnosis Based on Tree-Structured Labels and Hierarchical Multi-Granularity Diagnostic Network
by Hehua Yan, Jinbiao Tan, Yixiong Luo, Shiyong Wang and Jiafu Wan
Machines 2024, 12(12), 891; https://doi.org/10.3390/machines12120891 - 6 Dec 2024
Viewed by 678
Abstract
The aim of this study is to improve the cross-condition domain adaptability of bearing fault diagnosis models and their diagnostic performance under previously unknown conditions. Thus, this paper proposes a multi-condition adaptive bearing fault diagnosis method based on multi-granularity data annotation. A tree-structured [...] Read more.
The aim of this study is to improve the cross-condition domain adaptability of bearing fault diagnosis models and their diagnostic performance under previously unknown conditions. Thus, this paper proposes a multi-condition adaptive bearing fault diagnosis method based on multi-granularity data annotation. A tree-structured labeling scheme is introduced to allow for multi-granularity fault annotation. A hierarchical multi-granularity diagnostic network is designed to automatically learn multi-level fault information from condition data using feature extractors of varying granularity, allowing for the extraction of shared fault information across conditions. Additionally, a multi-granularity fault loss function is developed to help the deep network learn tree-structured labels, improving intra-class compactness and reducing hierarchical similarity between classes. Two experimental cases demonstrate that the proposed method exhibits robust cross-condition domain adaptability and performs better in unseen conditions than state-of-the-art methods. Full article
(This article belongs to the Special Issue AI-Driven Reliability Analysis and Predictive Maintenance)
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22 pages, 8560 KiB  
Article
Adaptive Dynamic Thresholding Method for Fault Detection in Diesel Engine Lubrication Systems
by Tingting Wu, Hongliang Song, Hongli Gao, Zongshen Wu and Feifei Han
Machines 2024, 12(12), 895; https://doi.org/10.3390/machines12120895 - 6 Dec 2024
Viewed by 741
Abstract
Fault detection in marine diesel engine lubrication systems is crucial for ensuring the long-term stable operation of diesel engines and the safety of maritime navigation. Traditional fixed-parameter alarm threshold methods lack flexibility and are prone to missing faults. Data-driven approaches like machine learning [...] Read more.
Fault detection in marine diesel engine lubrication systems is crucial for ensuring the long-term stable operation of diesel engines and the safety of maritime navigation. Traditional fixed-parameter alarm threshold methods lack flexibility and are prone to missing faults. Data-driven approaches like machine learning require high-quality data for fault samples. This study leverages the relative advantages of data mining methods and threshold techniques, proposing an adaptive threshold construction method based on dynamic parameter relationship inference. Employing an algorithm for inferring dynamic relationships among multiple parameters of the lubrication system builds an adaptive threshold detection model. Extensive diesel engine tests and actual fault data demonstrate that the proposed method can address the issues of missed faults encountered by static threshold methods and the low detection accuracy of machine learning approaches without the need for fault samples. This significantly enhances fault detection accuracy in marine diesel engine lubrication systems, offering considerable industrial practical value. Full article
(This article belongs to the Special Issue Machinery Condition Monitoring and Intelligent Fault Diagnosis)
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25 pages, 4153 KiB  
Article
Enhanced Fault Detection in Satellite Attitude Control Systems Using LSTM-Based Deep Learning and Redundant Reaction Wheels
by Sajad Saraygord Afshari
Machines 2024, 12(12), 856; https://doi.org/10.3390/machines12120856 - 27 Nov 2024
Cited by 1 | Viewed by 891
Abstract
Reliable fault detection in satellite attitude control systems stands as a critical aspect of ensuring the safety and success of space missions. Central to these systems, reaction wheels (RWs), despite being the most frequently used actuators, present a vulnerability given their susceptibility to [...] Read more.
Reliable fault detection in satellite attitude control systems stands as a critical aspect of ensuring the safety and success of space missions. Central to these systems, reaction wheels (RWs), despite being the most frequently used actuators, present a vulnerability given their susceptibility to faults—a factor with the potential to precipitate catastrophic failures such as total satellite loss. In light of this, we introduce a fault detection methodology grounded in deep learning techniques specifically designed for satellite attitude control systems. Our proposed method utilizes a Long Short-Term Memory (LSTM) model adept at learning temporal patterns inherent to both healthy and faulty system behaviors. Incorporated into our model is a torque allocation algorithm designed to circumvent specific velocities known to induce torque disturbances, a factor known to influence LSTM performance adversely. To bolster the robustness of our fault detection technique, we also incorporated denoising autoencoders within the LSTM framework, thereby enabling the model to identify temporal patterns in healthy and faulty system behavior, even amidst the noise. The method was evaluated using cross-validation on simulated satellite data comprising 1000 time series samples and across different fault scenarios, such as stiction and resonance at varying intensities (90%, 50%, and 30%). The results confirm achieving performance metrics such as Mean Squared Error for accurate fault identification. This research underscores a stride in the evolution of fault detection and control strategies for satellite attitude control systems, holding promise to boost the reliability and efficiency of future space missions. Full article
(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control of Power Machinery: Developments and Challenges)
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19 pages, 7892 KiB  
Article
Detection of Damage on Inner and Outer Races of Ball Bearings Using a Low-Cost Monitoring System and Deep Convolution Neural Networks
by Handeul You, Dongyeon Kim, Juchan Kim, Keunu Park and Sangjin Maeng
Machines 2024, 12(12), 843; https://doi.org/10.3390/machines12120843 - 25 Nov 2024
Viewed by 2907
Abstract
Bearings are vital components in machinery, and their malfunction can result in equipment damage and reduced productivity. As a result, considerable research attention has been directed toward the early detection of bearing faults. With recent rapid advancements in machine learning algorithms, there is [...] Read more.
Bearings are vital components in machinery, and their malfunction can result in equipment damage and reduced productivity. As a result, considerable research attention has been directed toward the early detection of bearing faults. With recent rapid advancements in machine learning algorithms, there is increasing interest in proactively diagnosing bearing faults by analyzing signals obtained from bearings. Although numerous studies have introduced machine learning methods for bearing fault diagnosis, the high costs associated with sensors and data acquisition devices limit their practical application in industrial environments. Additionally, research aimed at identifying the root causes of faults through diagnostic algorithms has progressed relatively slowly. This study proposes a cost-effective monitoring system to improve economic feasibility. Its primary benefits include significant cost savings compared to traditional high-priced equipment, along with versatility and ease of installation, enabling straightforward attachment and removal. The system collects data by measuring the vibrations of both normal and faulty bearings under various operating conditions on a test bed. Using these data, a deep neural network is trained to enable real-time feature extraction and classification of bearing conditions. Furthermore, an explainable AI technique is applied to extract key feature values identified by the fault classification algorithm, providing a method to support the analysis of fault causes. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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16 pages, 3622 KiB  
Article
A Soft Start Method for Doubly Fed Induction Machines Based on Synchronization with the Power System at Standstill Conditions
by José M. Guerrero, Kumar Mahtani, Itxaso Aranzabal, Julen Gómez-Cornejo, José A. Sánchez and Carlos A. Platero
Machines 2024, 12(12), 847; https://doi.org/10.3390/machines12120847 - 25 Nov 2024
Cited by 1 | Viewed by 720
Abstract
Due to their exceptional operational versatility, doubly fed induction machines (DFIM) are widely employed in power systems comprising variable renewable energy-based electrical generation sources, such as wind farms and pumped-storage hydropower plants. However, their starting and grid synchronization methods require numerous maneuvers or [...] Read more.
Due to their exceptional operational versatility, doubly fed induction machines (DFIM) are widely employed in power systems comprising variable renewable energy-based electrical generation sources, such as wind farms and pumped-storage hydropower plants. However, their starting and grid synchronization methods require numerous maneuvers or additional components, making the process challenging. In this paper, a soft start method for DFIM, inspired by the traditional synchronization method of synchronous machines, is proposed. This method involves matching the frequencies, voltages, and phase angles on both sides of the main circuit breaker, by adjusting the excitation through the controlled power converter at standstill conditions. Once synchronization is achieved, the frequency is gradually reduced to the rated operational levels. This straightforward starting method effectively suppresses large inrush currents and voltage sags. The proposed method has been validated through computer simulations and experimental tests, yielding satisfactory results. Full article
(This article belongs to the Section Electrical Machines and Drives)
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16 pages, 8804 KiB  
Article
Research on Unbalanced Vibration Characteristics and Assembly Phase Angle Probability Distribution of Dual-Rotor System
by Hui Li, Changzhi Shi, Xuejun Li, Mingfeng Li and Jie Bian
Machines 2024, 12(12), 842; https://doi.org/10.3390/machines12120842 - 24 Nov 2024
Viewed by 567
Abstract
This paper addresses the complex issue of vibration response characteristics resulting from the unbalanced assembly of the double rotors in the 31F aero-engine. The study investigates the vibration response behavior of the dual-rotor system through the adjustment of rotor assembly phase angle. Initially, [...] Read more.
This paper addresses the complex issue of vibration response characteristics resulting from the unbalanced assembly of the double rotors in the 31F aero-engine. The study investigates the vibration response behavior of the dual-rotor system through the adjustment of rotor assembly phase angle. Initially, a dynamic model of the four-disk, five-pivot dual-rotor system is established, with its natural frequencies and vibration modes verified. The influence of size and the position of the unbalance on the vibration amplitude in the dual-rotor system is analyzed. Additionally, the probability distribution of the assembly phase angles for both the compressor and turbine sections of the low-pressure rotor is examined. The results indicate that for the low-pressure rotor exhibiting excessive vibration, adjusting the assembly phase angle of the rotors’ system’s compressor or the turbine section by 180 degrees leads to a vibration qualification rate of 70.1435%. This finding is consistent with the observations from the field experience method used in the former Soviet Union. Finally, corresponding experimental verification is conducted. Full article
(This article belongs to the Special Issue Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis)
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18 pages, 3422 KiB  
Article
Use of Image Recognition and Machine Learning for the Automatic and Objective Evaluation of Standstill Marks on Rolling Bearings
by Markus Grebe, Alexander Baral and Dominik Martin
Machines 2024, 12(12), 840; https://doi.org/10.3390/machines12120840 - 23 Nov 2024
Viewed by 701
Abstract
One main research area of the Competence Centre for Tribology is so-called standstill marks (SSMs) at roller bearings that occur if the bearing is exposed to vibrations or performs just micromovements. SSMs obtained from experiments are usually photographed, evaluated and manually categorized into [...] Read more.
One main research area of the Competence Centre for Tribology is so-called standstill marks (SSMs) at roller bearings that occur if the bearing is exposed to vibrations or performs just micromovements. SSMs obtained from experiments are usually photographed, evaluated and manually categorized into six classes. An internal project has now investigated the extent to which this evaluation can be automated and objectified. Images of standstill marks were classified using convolutional neural networks that were implemented with the deep learning library Pytorch. With basic convolutional neural networks, an accuracy of 70.19% for the classification of all six classes and 83.65% for the classification of pairwise classes was achieved. Classification accuracies were improved by image augmentation and transfer learning with pre-trained convolutional neural networks. Overall, an accuracy of 83.65% for the classification of all six standstill mark classes and 91.35% for the classification of pairwise classes was achieved. Since 16 individual marks are generated per test run in a typical quasi standstill test (QSST) of the CCT and the deviation in the prediction of the classification is a maximum of one school grade, the accuracy achieved is already sufficient to carry out a reliable and objective evaluation of the markings. Full article
(This article belongs to the Special Issue Remaining Useful Life Prediction for Rolling Element Bearings)
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41 pages, 7143 KiB  
Review
Overview of IoT Security Challenges and Sensors Specifications in PMSM for Elevator Applications
by Eftychios I. Vlachou, Vasileios I. Vlachou, Dimitrios E. Efstathiou and Theoklitos S. Karakatsanis
Machines 2024, 12(12), 839; https://doi.org/10.3390/machines12120839 - 22 Nov 2024
Cited by 1 | Viewed by 1409
Abstract
The applications of the permanent magnet synchronous motor (PMSM) are the most seen in the elevator industry due to their high efficiency, low losses and the potential for high energy savings. The Internet of Things (IoT) is a modern technology which is being [...] Read more.
The applications of the permanent magnet synchronous motor (PMSM) are the most seen in the elevator industry due to their high efficiency, low losses and the potential for high energy savings. The Internet of Things (IoT) is a modern technology which is being incorporated in various industrial applications, especially in electrical machines as a means of control, monitoring and preventive maintenance. This paper is focused on reviewing the use PMSM in lift systems, the application of various condition monitoring techniques and real-time data collection techniques using IoT technology. In addition, we focus on different categories of industrial sensors, their connectivity and the standards they should meet for PMSMs used in elevator applications. Finally, we analyze various secure ways of transmitting data on different platforms so that the transmission of information takes into account possible unwanted instructions from exogenous factors. Full article
(This article belongs to the Special Issue Advancements in Condition Monitoring of Electric Motors: Integrating Digital Twins, AI, and IoT for Enhanced Operational Efficiency, Fault Diagnosis, and Cybersecurity)
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15 pages, 3119 KiB  
Article
Fault Detection in Harmonic Drive Using Multi-Sensor Data Fusion and Gravitational Search Algorithm
by Nan-Kai Hsieh and Tsung-Yu Yu
Machines 2024, 12(12), 831; https://doi.org/10.3390/machines12120831 - 21 Nov 2024
Viewed by 872
Abstract
This study proposes a fault diagnosis method for harmonic drive systems based on multi-sensor data fusion and the gravitational search algorithm (GSA). As a critical component in robotic arms, harmonic drives are prone to failures due to wear, less grease, or improper loading, [...] Read more.
This study proposes a fault diagnosis method for harmonic drive systems based on multi-sensor data fusion and the gravitational search algorithm (GSA). As a critical component in robotic arms, harmonic drives are prone to failures due to wear, less grease, or improper loading, which can compromise system stability and production efficiency. To enhance diagnostic accuracy, the research employs wavelet packet decomposition (WPD) and empirical mode decomposition (EMD) to extract multi-scale features from vibration signals. These features are subsequently fused, and GSA is used to optimize the high-dimensional fused features, eliminating redundant data and mitigating overfitting. The optimized features are then input into a support vector machine (SVM) for fault classification, with K-fold cross-validation used to assess the model’s generalization capabilities. Experimental results demonstrate that the proposed diagnosis method, which integrates multi-sensor data fusion with GSA optimization, significantly improves fault diagnosis accuracy compared to methods using single-sensor signals or unoptimized features. This improvement is particularly notable in multi-class fault scenarios. Additionally, GSA’s global search capability effectively addresses overfitting issues caused by high-dimensional data, resulting in a diagnostic model with greater reliability and accuracy across various fault conditions. Full article
(This article belongs to the Special Issue Compensation for Geometric Errors and Improvement in Accuracy through Innovative Design and Advanced Control Strategies)
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20 pages, 5509 KiB  
Article
Adaptive Multi-Scale Bayesian Framework for MFL Inspection of Steel Wire Ropes
by Xiaoping Li, Yujie Sun, Xinyue Liu and Shaoxuan Zhang
Machines 2024, 12(11), 801; https://doi.org/10.3390/machines12110801 - 12 Nov 2024
Viewed by 745
Abstract
Magnetic flux leakage (MFL) technology is widely used in steel wire rope (SWR) inspection for non-destructive testing. However, accurate defect characterization requires advanced signal processing techniques to handle complex noise conditions and varying defect types. This paper presents a novel adaptive multi-scale Bayesian [...] Read more.
Magnetic flux leakage (MFL) technology is widely used in steel wire rope (SWR) inspection for non-destructive testing. However, accurate defect characterization requires advanced signal processing techniques to handle complex noise conditions and varying defect types. This paper presents a novel adaptive multi-scale Bayesian framework for MFL signal analysis in SWR inspection. Our approach integrates discrete wavelet transform with adaptive thresholding and multi-scale feature fusion, enabling simultaneous detection of minute defects and large-area corrosion. To validate our method, we implemented a four-channel MFL detection system and conducted extensive experiments on both simulated and real-world datasets. Compared with state-of-the-art methods, including long short-term memory (LSTM), attention mechanisms, and isolation forests, our approach demonstrated significant improvements in precision, recall, and F1 score across various tolerance levels. The proposed method showed superior detection performance, with an average precision of 91%, recall of 89%, and an F1 score of 0.90 in high-noise conditions, surpassing existing techniques. Notably, our method showed superior performance in high-noise environments, reducing false positive rates while maintaining high detection sensitivity. While computational complexity in real-time processing remains a challenge, this study provides a robust solution for non-destructive testing of SWR, potentially improving inspection efficiency and defect localization accuracy. Future work will focus on optimizing algorithmic efficiency and exploring transfer learning techniques for enhanced adaptability across different non-destructive testing (NDT) domains. This research not only advances signal processing and anomaly detection technology but also contributes to enhancing safety and maintenance efficiency in critical infrastructure. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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13 pages, 6682 KiB  
Article
Design of a Thermal Performance Test Equipment for a High-Temperature and High-Pressure Heat Exchanger in an Aero-Engine
by Wongeun Yun, Manyeong Ha, Kuisoon Kim and Geesoo Lee
Machines 2024, 12(11), 794; https://doi.org/10.3390/machines12110794 - 10 Nov 2024
Viewed by 922
Abstract
For next-generation power systems, particularly aero-gas turbine engines, ultra-light and highly efficient heat exchangers are considered key enabling technologies for realizing advanced cycles. Consequently, the development of efficient and accurate aero-engine heat exchanger test equipment is essential to support future gas turbine heat [...] Read more.
For next-generation power systems, particularly aero-gas turbine engines, ultra-light and highly efficient heat exchangers are considered key enabling technologies for realizing advanced cycles. Consequently, the development of efficient and accurate aero-engine heat exchanger test equipment is essential to support future gas turbine heat exchanger advancements. This paper presents the development of a high-pressure and high-temperature (HPHT) heat exchanger test facility designed for aero-engine heat exchangers. The maximum temperature and pressure of the test facility were configured to simulate the conditions of the last-stage compressor of a large civil engine, specifically 1000 K and 5.5 MPa. These conditions were achieved using multiple electric heater systems in conjunction with an air compression system consisting of three turbo compressor units and a reciprocating compressor unit. A commissioning test was conducted using a compact tubular heat exchanger, and the results indicate that the test facility operates stably and that the measured data closely align with the predicted performance of the heat exchanger. A commissioning test of the tubular heat exchanger showed a thermal imbalance of 1.02% between the high-pressure (HP) and low-pressure (LP) lines. This level of imbalance is consistent with the ISO standard uncertainty of ±2.3% for heat dissipation. In addition, CFD simulation results indicated an average deviation of approximately 1.4% in the low-pressure outlet temperature. The close alignment between experimental and CFD results confirms the theoretical reliability of the test bench. The HPHT thermal performance test facility will be expected to serve as a critical test bed for evaluating heat exchangers for current and future gas turbine applications. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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16 pages, 7033 KiB  
Article
Influence of Distributor Structure on Through-Sea Valve Vibration Characteristics and Improvement Design
by Qingchao Yang, Zebin Li, Aimin Diao and Zhaozhao Ma
Machines 2024, 12(11), 791; https://doi.org/10.3390/machines12110791 - 8 Nov 2024
Viewed by 540
Abstract
To address the issue of excessive transient noise during the opening and closing of a sea valve, a method for reducing pressure fluctuations during the opening of the electromagnetic hydraulic distributor has been proposed by analyzing the structure and working principle of the [...] Read more.
To address the issue of excessive transient noise during the opening and closing of a sea valve, a method for reducing pressure fluctuations during the opening of the electromagnetic hydraulic distributor has been proposed by analyzing the structure and working principle of the distributor. Based on theoretical calculation and simulation analysis, the size and shape of the buffer slot of the flow hole are determined under the condition that the stable working flow rate remains unchanged. An improved electromagnetic hydraulic distributor is developed and tested. The results indicate that this method can effectively control the opening and closing transient noise of the sea valve. Full article
(This article belongs to the Special Issue Advances in Noises and Vibrations for Machines)
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21 pages, 16720 KiB  
Article
An Enhanced Spectral Amplitude Modulation Method for Fault Diagnosis of Rolling Bearings
by Zongcai Ma, Yongqi Chen, Tao Zhang and Ziyang Liao
Machines 2024, 12(11), 779; https://doi.org/10.3390/machines12110779 - 6 Nov 2024
Viewed by 569
Abstract
As a classic nonlinear filtering method, Spectral Amplitude Modulation (SAM) is widely used in the field of bearing fault characteristic frequency identification. However, when the vibration signal contains high-intensity noise interference, the accuracy of SAM in identifying fault characteristic frequencies is greatly reduced. [...] Read more.
As a classic nonlinear filtering method, Spectral Amplitude Modulation (SAM) is widely used in the field of bearing fault characteristic frequency identification. However, when the vibration signal contains high-intensity noise interference, the accuracy of SAM in identifying fault characteristic frequencies is greatly reduced. To solve the above problems, a Data Enhancement Spectral Amplitude Modulation (DA-SAM) method is proposed. This method further processes the modified signal through improved wavelet transform (IWT), calculates its logarithmic maximum square envelope spectrum to replace the original square envelope spectrum, and finally completes SAM. By highlighting signal characteristics and strengthening feature information, interference information can be minimized, thereby improving the robustness of the SAM method. In this paper, this method is verified through fault data sets. The research results show that this method can effectively reduce the interference of noise on fault diagnosis, and the fault characteristic information obtained is clearer. The superiority of this method compared with the SAM method, Autogram method, and fast spectral kurtosis diagram method is proved. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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29 pages, 53780 KiB  
Article
Comprehensive Analysis of Major Fault-to-Failure Mechanisms in Harmonic Drives
by Roberto Guida, Antonio Carlo Bertolino, Andrea De Martin and Massimo Sorli
Machines 2024, 12(11), 776; https://doi.org/10.3390/machines12110776 - 5 Nov 2024
Cited by 1 | Viewed by 1679
Abstract
The present paper proposes a detailed Failure Mode, Effects, and Criticality Analysis (FMECA) on harmonic drives, focusing on their integration within the UR5 cobot. While harmonic drives are crucial for precision and efficiency in robotic manipulators, they are also prone to several failure [...] Read more.
The present paper proposes a detailed Failure Mode, Effects, and Criticality Analysis (FMECA) on harmonic drives, focusing on their integration within the UR5 cobot. While harmonic drives are crucial for precision and efficiency in robotic manipulators, they are also prone to several failure modes that may affect the overall reliability of a system. This work provides a comprehensive analysis intended as a benchmark for advancements in predictive maintenance and condition-based monitoring. The results not only offer insights into improving the operational lifespan of harmonic drives, but also provide guidance for engineers working with similar systems across various robotic platforms. Robotic systems have advanced significantly; however, maintaining their reliability is essential, especially in industrial applications where even minor faults can lead to costly downtimes. This article examines the impact of harmonic drive degradation on industrial robots, with a focus on collaborative robotic arms. Condition-Based Maintenance (CBM) and Prognostics and Health Management (PHM) approaches are discussed, highlighting how digital twins and data-driven models can enhance fault detection. A case study using the UR5 collaborative robot illustrates the importance of fault diagnosis in harmonic drives. The analysis of fault-to-failure mechanisms, including wear, pitting, and crack propagation, shows how early detection strategies, such as vibration analysis and proactive maintenance approaches, can improve system reliability. The findings offer insights into failure mode identification, criticality analysis, and recommendations for improving fault tolerance in robotic systems. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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19 pages, 3670 KiB  
Article
Modal Parameter Identification of Electric Spindles Based on Covariance-Driven Stochastic Subspace
by Wenhong Zhou, Liuzhou Zhong, Weimin Kang, Yuetong Xu, Congcong Luan and Jianzhong Fu
Machines 2024, 12(11), 774; https://doi.org/10.3390/machines12110774 - 4 Nov 2024
Viewed by 974
Abstract
Electric spindles are a critical component of numerically controlled machine tools that directly affect machining precision and efficiency. The accurate identification of the modal parameters of an electric spindle is essential for optimizing design, enhancing dynamic performance, and facilitating fault diagnosis. This study [...] Read more.
Electric spindles are a critical component of numerically controlled machine tools that directly affect machining precision and efficiency. The accurate identification of the modal parameters of an electric spindle is essential for optimizing design, enhancing dynamic performance, and facilitating fault diagnosis. This study proposes a covariance-driven stochastic subspace identification (SSI-cov) method integrated with a simulated annealing (SA) strategy and fuzzy C-means (FCM) clustering algorithm to achieve the automated identification of modal parameters for electric spindles. Using both finite element simulations and experimental tests conducted at 22 °C, the first five natural frequencies of the electric spindle under free, constrained, and dynamic conditions were extracted. The experimental results demonstrated experiment errors of 0.17% to 0.33%, 1.05% to 3.27%, and 1.29% to 3.31% for the free, constrained, and dynamic states, respectively. Compared to the traditional SSI-cov method, the proposed SA-FCM method improved accuracy by 12.05% to 27.32% in the free state, 17.45% to 47.83% in the constrained state, and 25.45% to 49.12% in the dynamic state. The frequency identification errors were reduced to a range of 2.25 Hz to 20.81 Hz, significantly decreasing errors in higher-order modes and demonstrating the robustness of the algorithm. The proposed method required no manual intervention, and it could be utilized to accurately analyze the modal parameters of electric spindles under free, constrained, and dynamic conditions, providing a precise and reliable solution for the modal analysis of electric spindles in various dynamic states. Full article
(This article belongs to the Special Issue Error Measurement, Analysis, and Compensation Technology for CNC Machine Tools)
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20 pages, 3602 KiB  
Article
Effective Machine Learning Solution for State Classification and Productivity Identification: Case of Pneumatic Pressing Machine
by Alexandros Kolokas, Panagiotis Mallioris, Michalis Koutsiantzis, Christos Bialas, Dimitrios Bechtsis and Evangelos Diamantis
Machines 2024, 12(11), 762; https://doi.org/10.3390/machines12110762 - 30 Oct 2024
Cited by 2 | Viewed by 972
Abstract
The fourth industrial revolution (Industry 4.0) brought significant changes in manufacturing, driven by technologies like artificial intelligence (AI), Internet of Things (IoT), 5G, robotics, and big data analytics. For industries to remain competitive, the primary goals must be the improvement of the efficiency [...] Read more.
The fourth industrial revolution (Industry 4.0) brought significant changes in manufacturing, driven by technologies like artificial intelligence (AI), Internet of Things (IoT), 5G, robotics, and big data analytics. For industries to remain competitive, the primary goals must be the improvement of the efficiency and safety of machinery, the reduction of production costs, and the enhancement of product quality. Predictive maintenance (PdM) utilizes historical data and AI models to diagnose equipment’s health and predict the remaining useful life (RUL), providing critical insights for machinery effectiveness and product manufacturing. This prediction is a critical strategy to maximize the useful life of equipment, especially in large-scale and important infostructures. This study focuses on developing an unsupervised machine state-classification solution utilizing real-world industrial measurements collected from a pneumatic pressing machine. Unsupervised machine learning (ML) models were tested to diagnose and output the working state of the pressing machine at each given point (offline, idle, pressing, defective). Our research contributes to extracting valuable insights regarding real-world industrial settings for PdM and production efficiency using unsupervised ML, promoting operation safety, cost reduction, and productivity enhancement in modern industries. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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21 pages, 7354 KiB  
Article
Visual-Inertial Fusion-Based Five-Degree-of-Freedom Motion Measurement System for Vessel-Mounted Cranes
by Boyang Yu, Yuansheng Cheng, Xiangjun Xia, Pengfei Liu, Donghong Ning and Zhixiong Li
Machines 2024, 12(11), 748; https://doi.org/10.3390/machines12110748 - 23 Oct 2024
Viewed by 1084
Abstract
Vessel-mounted cranes operate in complex marine environments, where precise measurement of cargo positions and attitudes is a key technological challenge to ensure operational stability and safety. This study introduces an integrated measurement system that combines vision and inertial sensing technologies, utilizing a stereo [...] Read more.
Vessel-mounted cranes operate in complex marine environments, where precise measurement of cargo positions and attitudes is a key technological challenge to ensure operational stability and safety. This study introduces an integrated measurement system that combines vision and inertial sensing technologies, utilizing a stereo camera and two inertial measurement units (IMUs) to capture cargo motion in five degrees of freedom (DOF). By merging data from the stereo camera and IMUs, the system accurately determines the cargo’s position and attitude relative to the camera. The specific methodology is introduced as follows: First, the YOLO model is adopted to identify targets in the image and generate bounding boxes. Then, using the principle of binocular disparity, the depth within the bounding box is calculated to determine the target’s three-dimensional position in the camera coordinate system. Simultaneously, the IMU measures the attitude of the cargo, and a Kalman filter is applied to fuse the data from the two sensors. Experimental results indicate that the system’s measurement errors in the x, y, and z directions are less than 2.58%, 3.35%, and 3.37%, respectively, while errors in the roll and pitch directions are 3.87% and 5.02%. These results demonstrate that the designed measurement system effectively provides the necessary motion information in 5-DOF for vessel-mounted crane control, offering new approaches for pose detection of marine cranes and cargoes. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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15 pages, 2222 KiB  
Article
LSTM-Inversion-Based Feedforward–Feedback Nanopositioning Control
by Ruocheng Yin and Juan Ren
Machines 2024, 12(11), 747; https://doi.org/10.3390/machines12110747 - 22 Oct 2024
Viewed by 748
Abstract
This work proposes a two-degree of freedom (2DOF) controller for motion tracking of nanopositioning devices, such as piezoelectric actuators (PEAs), with a broad bandwidth and high precision. The proposed 2DOF controller consists of an inversion feedforward controller and a real-time feedback controller. The [...] Read more.
This work proposes a two-degree of freedom (2DOF) controller for motion tracking of nanopositioning devices, such as piezoelectric actuators (PEAs), with a broad bandwidth and high precision. The proposed 2DOF controller consists of an inversion feedforward controller and a real-time feedback controller. The feedforward controller, a sequence-to-sequence LSTM-based inversion model (invLSTMs2s), is used to compensate for the nonlinearity of the PEA, especially at high frequencies, and is collaboratively integrated with a linear MPC feedback controller, which ensures the PEA position tracking performance at low frequencies. Therefore, the proposed 2DOF controller, namely, invLSTMs2s+MPC, is able to achieve high precision over a broad bandwidth. To validate the proposed controller, the uncertainty of invLSTMs2s is checked such that the integration of an inversion model-based feedforward controller has a positive impact on the trajectory tracking performance compared to feedback control only. Experimental validation on a commercial PEA and comparison with existing approaches demonstrate that high tracking accuracies can be achieved by invLSTMs2s+MPC for various reference trajectories. Moreover, invLSTMs2s+MPC is further demonstrated on a multi-dimensional PEA platform for simultaneous multi-direction positioning control. Full article
(This article belongs to the Special Issue Advances in Applied Mechatronics, Volume II)
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23 pages, 10799 KiB  
Article
The Development and Experimental Validation of a Real-Time Coupled Gear Wear Prediction Model Considering Initial Surface Topography, Dynamics, and Thermal Deformation
by Jingqi Zhang, Jianxing Zhou, Quanwei Cui, Ning Dong, Hong Jiang and Zhong Fang
Machines 2024, 12(10), 734; https://doi.org/10.3390/machines12100734 - 17 Oct 2024
Viewed by 1022
Abstract
Errors affect the actual meshing process of gears, alter the actual wear pattern of the tooth profile, and may even impact the overall service life of machinery. While existing research predominantly focuses on individual errors or a narrow set of factors, this study [...] Read more.
Errors affect the actual meshing process of gears, alter the actual wear pattern of the tooth profile, and may even impact the overall service life of machinery. While existing research predominantly focuses on individual errors or a narrow set of factors, this study explores the combined effects of multiple errors on tooth profile wear. A comprehensive gear wear prediction model was developed, integrating the slice method, lumped mass method, Hertz contact model, and Archard’s wear theory. This model accounts for initial tooth surface topography, thermal deformation, dynamic effects, and wear, establishing strong correlations between gear wear prediction and key factors such as tooth surface morphology, temperature, and vibration. Experimental validation demonstrated the model’s high accuracy, with relatively small deviations from the observed wear. Initial profile errors (IPEs) at different positions along the tooth width result in varying relative sliding distances, leading to differences in wear depth despite a consistent overall trend. Notably, large IPEs at the dedendum and addendum can influence wear progression, either accelerating or decelerating the wear process over time. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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17 pages, 11316 KiB  
Article
Thermal Error Transfer Prediction Modeling of Machine Tool Spindle with Self-Attention Mechanism-Based Feature Fusion
by Yue Zheng, Guoqiang Fu, Sen Mu, Caijiang Lu, Xi Wang and Tao Wang
Machines 2024, 12(10), 728; https://doi.org/10.3390/machines12100728 - 15 Oct 2024
Viewed by 1155
Abstract
Thermal errors affect machining accuracy in high-speed precision machining. The variability of machine tool operating conditions poses a challenge to the modeling of thermal errors. In this paper, a thermal error model based on transfer temperature feature fusion is proposed. Firstly, the temperature [...] Read more.
Thermal errors affect machining accuracy in high-speed precision machining. The variability of machine tool operating conditions poses a challenge to the modeling of thermal errors. In this paper, a thermal error model based on transfer temperature feature fusion is proposed. Firstly, the temperature information fusion features are built as inputs to the model, which is based on a self-attention mechanism to assign weights to the temperature information and fuse the features. Secondly, an improved direct normalization-based adaptive matrix approach is proposed, updating the background matrix using an autoencoder and reconstructing the adaptive matrix to realize domain self-adaptation. In addition, for the improved adaptive matrix, a criterion for determining whether the working conditions are transferrable to each other is proposed. The proposed method shows high prediction accuracy while ensuring training efficiency. Finally, thermal error experiments are performed on a VCM850 CNC machine tool. Full article
(This article belongs to the Special Issue Error Measurement, Analysis, and Compensation Technology for CNC Machine Tools)
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22 pages, 7423 KiB  
Article
Advancing UAV Sensor Fault Diagnosis Based on Prior Knowledge and Graph Convolutional Network
by Hui Li, Chaoyin Chen, Tiancai Wan, Shaoshan Sun, Yongbo Li and Zichen Deng
Machines 2024, 12(10), 716; https://doi.org/10.3390/machines12100716 - 10 Oct 2024
Viewed by 909
Abstract
Unmanned aerial vehicles (UAVs) are equipped with various sensors to facilitate control and navigation. However, UAV sensors are highly susceptible to damage under complex flight environments, leading to severe accidents and economic losses. Although fault diagnosis methods based on deep neural networks have [...] Read more.
Unmanned aerial vehicles (UAVs) are equipped with various sensors to facilitate control and navigation. However, UAV sensors are highly susceptible to damage under complex flight environments, leading to severe accidents and economic losses. Although fault diagnosis methods based on deep neural networks have been widely applied in the mechanical field, these methods often fail to integrate multi-source information and overlook the importance of system prior knowledge. As a result, this study employs a spatial-temporal difference graph convolutional network (STDGCN) for the fault diagnosis of UAV sensors, where the graph structure naturally organizes the diverse sensors. Specifically, a difference layer enhances the feature extraction capability of the graph nodes, and the spatial-temporal graph convolutional modules are designed to extract spatial-temporal dependencies from sensor data. Moreover, to ensure the accuracy of the association graph, this research introduces the UAV’s dynamic model as prior knowledge for constructing the association graph. Finally, diagnostic accuracies of 94.93%, 98.71%, and 92.97% were achieved on three self-constructed datasets. In addition, compared to commonly used data-driven approaches, the proposed method demonstrates superior feature extraction capabilities and achieves the highest diagnostic accuracy. Full article
(This article belongs to the Special Issue Data-Driven Approaches regarding Dynamic Modelling, Diagnostics and Prognostics of Complex Mechanical Structures)
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17 pages, 13481 KiB  
Article
Detection of Broken Bars in Induction Motors Operating with Closed-Loop Speed Control
by Francesca Muzio, Lorenzo Mantione, Tomas Garcia-Calva, Lucia Frosini and Daniel Morinigo-Sotelo
Machines 2024, 12(9), 662; https://doi.org/10.3390/machines12090662 - 21 Sep 2024
Cited by 2 | Viewed by 954
Abstract
Rotor bar breakage in induction motors is often detected by analysing the signatures in the stator current. However, due to the alteration of the current spectrum, traditional methods may fail when inverter-fed motors operate with closed-loop control using a cascade structure to regulate [...] Read more.
Rotor bar breakage in induction motors is often detected by analysing the signatures in the stator current. However, due to the alteration of the current spectrum, traditional methods may fail when inverter-fed motors operate with closed-loop control using a cascade structure to regulate the speed. In this paper, the potential of zero-sequence voltage analysis to detect this fault is investigated, and a new index to quantify the severity of the fault based on this signal is proposed. Signals from motors operating under different control strategies and signals from motors powered from the mains are considered to verify the robustness of the proposed fault severity index. As a result, in all the analysed conditions the value of the proposed index for the healthy motor is found to be approximately 0.010, while for the faulty machine it is between 0.110 and 0.252. Full article
(This article belongs to the Section Electrical Machines and Drives)
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23 pages, 8375 KiB  
Article
Artificial-Intelligence-Based Condition Monitoring of Industrial Collaborative Robots: Detecting Anomalies and Adapting to Trajectory Changes
by Samuel Ayankoso, Fengshou Gu, Hassna Louadah, Hamidreza Fahham and Andrew Ball
Machines 2024, 12(9), 630; https://doi.org/10.3390/machines12090630 - 7 Sep 2024
Cited by 2 | Viewed by 1991
Abstract
The increasing use of collaborative robots in smart manufacturing, owing to their flexibility and safety benefits, underscores a critical need for robust predictive maintenance strategies to prevent unexpected faults/failures of the machine. This paper focuses on fault detection and employs multivariate operational data [...] Read more.
The increasing use of collaborative robots in smart manufacturing, owing to their flexibility and safety benefits, underscores a critical need for robust predictive maintenance strategies to prevent unexpected faults/failures of the machine. This paper focuses on fault detection and employs multivariate operational data from a universal robot to detect anomalies or early-stage faults using test data from designed anomalous conditions and artificial-intelligence-based anomaly detection techniques called autoencoders. The performance of three autoencoders, namely, a multi-layer-perceptron-based autoencoder, convolutional-neural-network-based autoencoder, and sparse autoencoder, was compared in detecting anomalies. The results indicate that the autoencoders effectively detected anomalies in the examined complex and noisy datasets with more than 93% overall accuracy and an F1 score exceeding 96% for the considered anomalous cases. Moreover, the integration of trajectory change detection and anomaly detection algorithms (i.e., the dynamic time warping algorithm and sparse autoencoder, respectively) was proposed for the local implementation of online condition monitoring. This integrated approach to anomaly detection and trajectory change provides a practical, adaptive, and economical solution for enhancing the reliability and safety of collaborative robots in smart manufacturing environments. Full article
(This article belongs to the Special Issue Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis)
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13 pages, 9787 KiB  
Article
A Study on the Static and Dynamic Characteristics of the Spindle System of a Spiral Bevel Gear Grinding Machine
by Shuai Huang, Juxin Wang, Kaifeng Huang and Jianwu Yu
Machines 2024, 12(9), 619; https://doi.org/10.3390/machines12090619 - 4 Sep 2024
Viewed by 1030
Abstract
To enhance the static and dynamic performance of the grinding wheel spindle system, with the gear grinding machine (YKF2060) as the research object, a static mechanics model of the spindle system was established based on Castigliano’s theorem, taking into account the equivalent effect [...] Read more.
To enhance the static and dynamic performance of the grinding wheel spindle system, with the gear grinding machine (YKF2060) as the research object, a static mechanics model of the spindle system was established based on Castigliano’s theorem, taking into account the equivalent effect of the triple-point contact ball bearing at the front end of the spindle. Meanwhile, based on the overall transfer matrix method, a dynamic model of the main spindle–eccentric shaft dual-rotor system was established, taking into account the effects of shear deformation and gyroscopic moments. On this basis, the effect of the spindle span, the front and rear overhang of the eccentric shaft, and the bearing stiffness on the static stiffness and first-order critical speed of the system was analyzed. Finally, static stiffness experiments, modal tests, and finite element simulation models were conducted to verify the static and dynamic models. The results show that the stiffness of the front outer bearing has the greatest influence on the static and dynamic performance of the system, while the stiffness of the rear inner bearing has the least influence. The relative errors of the static stiffness and the first two natural frequencies between static stiffness experiments, modal tests, and finite element simulation models are less than 10%, and the mode shapes match well. The established static and dynamic model can effectively reflect both the static and dynamic characteristics of the spindle system. Full article
(This article belongs to the Section Advanced Manufacturing)
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22 pages, 6340 KiB  
Article
Detecting Anomalies in Hydraulically Adjusted Servomotors Based on a Multi-Scale One-Dimensional Residual Neural Network and GA-SVDD
by Xukang Yang, Anqi Jiang, Wanlu Jiang, Yonghui Zhao, Enyu Tang and Zhiqian Qi
Machines 2024, 12(9), 599; https://doi.org/10.3390/machines12090599 - 28 Aug 2024
Viewed by 753
Abstract
A high-pressure hydraulically adjusted servomotor is an electromechanical–hydraulic integrated system centered on a servo valve that plays a crucial role in ensuring the safe and stable operation of steam turbines. To address the issues of difficult fault diagnoses and the low maintenance efficiency [...] Read more.
A high-pressure hydraulically adjusted servomotor is an electromechanical–hydraulic integrated system centered on a servo valve that plays a crucial role in ensuring the safe and stable operation of steam turbines. To address the issues of difficult fault diagnoses and the low maintenance efficiency of adjusted hydraulic servomotors, this study proposes a model for detecting abnormalities of hydraulically adjusted servomotors. This model uses a multi-scale one-dimensional residual neural network (M1D_ResNet) for feature extraction and a genetic algorithm (GA)-optimized support vector data description (SVDD). Firstly, the multi-scale features of the vibration signals of the hydraulically adjusted servomotor were extracted and fused using one-dimensional convolutional blocks with three different scales to construct a multi-scale one-dimensional residual neural network binary classification model capable of recognizing normal and abnormal states. Then, this model was used as a feature extractor to create a feature set of normal data. Finally, an abnormal detection model for the hydraulically adjusted servomotor was constructed by optimizing the support vector data domain based on this feature set using a genetic algorithm. The proposed method was experimentally validated on a hydraulically adjusted servomotor dataset. The results showed that, compared with the traditional single-scale one-dimensional residual neural network, the multi-scale feature vectors fused by the multi-scale one-dimensional convolutional neural network contained richer state-sensitive information, effectively improving the performance of detecting abnormalities in the hydraulically adjusted servomotor. Full article
(This article belongs to the Special Issue Advanced Control and Fault Detection Techniques in Hydraulic Machines and Systems)
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24 pages, 11901 KiB  
Article
The Workspace Analysis of the Delta Robot Using a Cross-Section Diagram Based on Zero Platform
by Jun-Ho Hong, Ji-Ho Lim, Euntaek Lee and Dongwon Shin
Machines 2024, 12(8), 583; https://doi.org/10.3390/machines12080583 - 22 Aug 2024
Cited by 1 | Viewed by 1412
Abstract
This paper introduces a new concept of a zero-platform delta robot with three key parameters affecting the shape and size of the workspace. This concept is applied to directly bring the torus configuration into the links of the robot and shows its usefulness [...] Read more.
This paper introduces a new concept of a zero-platform delta robot with three key parameters affecting the shape and size of the workspace. This concept is applied to directly bring the torus configuration into the links of the robot and shows its usefulness in configuring and generating the workspace conveniently. Analyzing the workspace of parallel robots, such as delta robots, requires extensive computation due to the constraints between the links, typically requiring complex equations or numerical methods. This paper proposes a new method for quickly estimating the shape and size of the workspace using a cross-section diagram based on a geometrical analysis of the zero-platform delta robot. The shape and size of the workspace can be rapidly estimated because the intersection of three cross-section diagrams needs only the torus’s 2D operation. Comparing the workspace between the cross-section diagram and the 3D CAD software, this paper shows that the cross-section diagram can analyze the shape and size of the workspace quickly and give a more geometrical understanding of the workspace. Full article
(This article belongs to the Section Machine Design and Theory)
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18 pages, 3470 KiB  
Article
Soft Robots: Computational Design, Fabrication, and Position Control of a Novel 3-DOF Soft Robot
by Martin Garcia, Andrea-Contreras Esquen, Mark Sabbagh, Devin Grace, Ethan Schneider, Turaj Ashuri, Razvan Cristian Voicu, Ayse Tekes and Amir Ali Amiri Moghadam
Machines 2024, 12(8), 539; https://doi.org/10.3390/machines12080539 - 7 Aug 2024
Cited by 1 | Viewed by 2215
Abstract
This paper presents the computational design, fabrication, and control of a novel 3-degrees-of-freedom (DOF) soft parallel robot. The design is inspired by a delta robot structure. It is engineered to overcome the limitations of traditional soft serial robot arms, which are typically low [...] Read more.
This paper presents the computational design, fabrication, and control of a novel 3-degrees-of-freedom (DOF) soft parallel robot. The design is inspired by a delta robot structure. It is engineered to overcome the limitations of traditional soft serial robot arms, which are typically low in structural stiffness and blocking force. Soft robotic systems are becoming increasingly popular due to their inherent compliance match to that of human body, making them an efficient solution for applications requiring direct contact with humans. The proposed soft robot consists of three soft closed-loop kinematic chains, each of which includes a soft actuator and a compliant four-bar arm. The complex nonlinear dynamics of the soft robot are numerically modeled, and the model is validated experimentally using a 6-DOF electromagnetic position sensor. This research contributes to the growing body of literature in the field of soft robotics, providing insights into the computational design, fabrication, and control of soft parallel robots for use in a variety of complex applications. Full article
(This article belongs to the Special Issue Mechanical Design, Mechatronics and Control of 3D-Printed Robots)
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17 pages, 7127 KiB  
Article
Computational Fluid Dynamics-Based Optimisation of High-Speed and High-Performance Bearingless Cross-Flow Fan Designs
by Ivana Bagaric, Daniel Steinert, Thomas Nussbaumer and Johann Walter Kolar
Machines 2024, 12(8), 513; https://doi.org/10.3390/machines12080513 - 29 Jul 2024
Viewed by 949
Abstract
To enhance the fluid dynamic performance of bearingless cross-flow fans (CFFs), this paper presents a CFD-based optimisation of both rotor and static casing wall modifications. High-performance CFFs are essential in industrial applications such as highly specialised laser modules in the semiconductor industry. The [...] Read more.
To enhance the fluid dynamic performance of bearingless cross-flow fans (CFFs), this paper presents a CFD-based optimisation of both rotor and static casing wall modifications. High-performance CFFs are essential in industrial applications such as highly specialised laser modules in the semiconductor industry. The goal for the investigated rotor modifications is to enhance the CFF’s mechanical stiffness by integrating reinforcing shafts, which is expected to increase the limiting bending resonance frequency, thereby permitting higher rotational speeds. Additionally, the effects of these rotor modifications on the fluid dynamic performance are evaluated. For the casing wall modifications, the goal is to optimise design parameters to reduce losses. Optimised bearingless CFFs benefit semiconductor manufacturing by improving the gas circulation system within the laser module. Higher CFF performance is a key enabler for enhancing laser performance, increasing the scanning speed of lithography machines, and ultimately improving chip throughput. Several numerical simulations are conducted and validated using various commissioned prototypes, each measuring 600mm in length and 60mm in outer diameter. The results reveal that integrating a central shaft increases the rotational speed by up to 42%, from 5000rpm to 7100rpm, due to enhanced CFF stiffness. However, the loss in fluid flow amounts to 61% and outweighs the gain in rotational speed. Optimising the casing walls results in a 22% increase in maximum fluid flow reaching 1800m3/h at 5000rpm. It is demonstrated that the performance of bearingless CFFs can be enhanced by modifying the geometry of the casing walls, without requiring changes to the CFF rotor or bearingless motors. Full article
(This article belongs to the Section Turbomachinery)
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16 pages, 2579 KiB  
Article
Experimental Evaluation of Acoustical Materials for Noise Reduction in an Induction Motor Drive
by Ashish Kumar Sahu, Abeka Selliah, Alaa Hassan, Moien Masoumi and Berker Bilgin
Machines 2024, 12(8), 499; https://doi.org/10.3390/machines12080499 - 23 Jul 2024
Cited by 3 | Viewed by 1403
Abstract
Electric propulsion motors are more efficient than internal combustion engines, but they generate high-frequency tonal noise, which can be perceived as annoying. Acoustical materials are typically suitable for high-frequency noise, making them ideal for acoustic noise mitigation. This paper investigates the effectiveness of [...] Read more.
Electric propulsion motors are more efficient than internal combustion engines, but they generate high-frequency tonal noise, which can be perceived as annoying. Acoustical materials are typically suitable for high-frequency noise, making them ideal for acoustic noise mitigation. This paper investigates the effectiveness of three acoustical materials, namely, 2″ Polyurethane foam, 2″ Vinyl-faced quilted glass fiber, and 2″ Studiofoam, in mitigating the acoustic noise from an induction motor and a variable frequency inverter. Acoustic noise rates at multiple motor speeds, with and without the application of acoustical materials, are compared to determine the effectiveness of acoustical materials in mitigating acoustic noise at the transmission stage. Acoustical materials reduce acoustic noise from the induction motor by 5–14 dB(A) at around 500 Hz and by 22–31 dB(A) at around 10,000 Hz. Among the tested materials, Studiofoam demonstrates superior noise absorption capacity across the entire frequency range. Polyurethane foam is a cost-effective and lightweight alternative, and it is equally as effective as Studifoam in mitigating high-frequency acoustic noise above 5000 Hz. Full article
(This article belongs to the Section Electrical Machines and Drives)
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16 pages, 14910 KiB  
Article
A Comparative Study of Pole–Slot Combination with Fractional Slot Concentrated Winding in Outer Rotor Permanent Magnet Synchronous Generator for Hybrid Drone System
by Jeongwon Kim, Ju Lee and Hyunwoo Kim
Machines 2024, 12(7), 464; https://doi.org/10.3390/machines12070464 - 10 Jul 2024
Cited by 1 | Viewed by 1356
Abstract
This paper is a comparative study of pole–slot combinations with fractional slot concentrated windings in an outer rotor permanent magnet synchronous generator (ORPMSG) for a hybrid drone system. Fractional slot machines have been studied for automotive applications because of their high performance and [...] Read more.
This paper is a comparative study of pole–slot combinations with fractional slot concentrated windings in an outer rotor permanent magnet synchronous generator (ORPMSG) for a hybrid drone system. Fractional slot machines have been studied for automotive applications because of their high performance and simple winding manufacturing, compared with those of integer slot machines. In this study, four pole–slot combinations of ORPMSG with fractional slot concentrated windings were selected for comparison with the performance of the hybrid drone system. Based on the results of a finite element analysis (FEA), the machines were analyzed for cogging torque, back electromagnetic force (BEMF), torque, and electromagnetic loss under the same conditions as the machine specifications. Among the four pole–slot combinations of the ORPMSM, a one pole–slot model of the ORPMSG was selected, considering the performances of the machines. The selected pole–slot model of the ORPMSG was manufactured, and experiments were conducted on the manufactured model to verify the FEA results. Finally, the effectiveness of the comparative study of pole–slot combination with fractional slot concentrated winding in ORPMSM was verified by comparing the FEA and experimental results. Full article
(This article belongs to the Section Electrical Machines and Drives)
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18 pages, 3548 KiB  
Article
Numerical and Experimental Study on Dummy Blade with Underplatform Damper
by Di Li, Chenhong Du, Hongguang Li and Guang Meng
Machines 2024, 12(7), 461; https://doi.org/10.3390/machines12070461 - 7 Jul 2024
Cited by 1 | Viewed by 1387
Abstract
To confirm the variation in damping ratio offered by dry friction dampers against structural vibration stress, this study developed a blade vibration response test system for capturing damping characteristic curves through both frequency sweep excitation and damping-freevibration methods. The damping-free vibration method demonstrates [...] Read more.
To confirm the variation in damping ratio offered by dry friction dampers against structural vibration stress, this study developed a blade vibration response test system for capturing damping characteristic curves through both frequency sweep excitation and damping-freevibration methods. The damping-free vibration method demonstrates high efficiency, allowing for the acquisition of a complete damping ratio characteristic curve in a single experiment. Experimental findings indicate that the two contact surfaces on the triangular prism damper produce distinct damping effects, closely aligning with the predicted damping characteristic curves. The peak damping ratio was found to be independent of the centrifugal load of the damper; dampers with varying contact areas produce approximately similar damping characteristics; and the damping effect shows a positive correlation with the root extension length. Full article
(This article belongs to the Special Issue Cutting-Edge Research in Tribology and Its Applications to Rolling Element Bearings)
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14 pages, 5333 KiB  
Article
Response Surface Methodology for Kinematic Design of Soft Pneumatic Joints: An Application to a Bio-Inspired Scorpion-Tail-Actuator
by Michele Gabrio Antonelli, Pierluigi Beomonte Zobel and Nicola Stampone
Machines 2024, 12(7), 439; https://doi.org/10.3390/machines12070439 - 26 Jun 2024
Cited by 2 | Viewed by 1546
Abstract
In soft robotics, the most used actuators are soft pneumatic actuators because of their simplicity, cost-effectiveness, and safety. However, pneumatic actuation is also disadvantageous because of the strong non-linearities associated with using a compressible fluid. The identification of analytical models is often complex, [...] Read more.
In soft robotics, the most used actuators are soft pneumatic actuators because of their simplicity, cost-effectiveness, and safety. However, pneumatic actuation is also disadvantageous because of the strong non-linearities associated with using a compressible fluid. The identification of analytical models is often complex, and finite element analyses are preferred to evaluate deformation and tension states, which are computationally onerous. Alternatively, artificial intelligence algorithms can be used to follow model-free and data-driven approaches to avoid modeling complexity. In this work, however, the response surface methodology was adopted to identify a predictive model of the bending angle for soft pneumatic joints through geometric and functional parameters. The factorial plan was scheduled based on the design of the experiment, minimizing the number of tests needed and saving materials and time. Finally, a bio-inspired application of the identified model is proposed by designing the soft joints and making an actuator that replicates the movements of the scorpion’s tail in the attack position. The model was validated with two external reinforcements to achieve the same final deformation at different feeding pressures. The average absolute errors between predicted and experimental bending angles for I and II reinforcement allowed the identified model to be verified. Full article
(This article belongs to the Special Issue Intelligent Bio-Inspired Robots: New Trends and Future Perspectives)
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17 pages, 8573 KiB  
Article
A Robust Online Diagnostic Strategy of Inverter Open-Circuit Faults for Robotic Joint BLDC Motors
by Mohamed Y. Metwly, Victor M. Logan, Charles L. Clark, Jiangbiao He and Biyun Xie
Machines 2024, 12(7), 430; https://doi.org/10.3390/machines12070430 - 24 Jun 2024
Viewed by 1359
Abstract
As robots are increasingly used in remote, safety-critical, and hazardous applications, the reliability of robots is becoming more important than ever before. Robotic arm joint motor-drive systems are vulnerable to hardware failures due to harsh operating environment in many scenarios, which may yield [...] Read more.
As robots are increasingly used in remote, safety-critical, and hazardous applications, the reliability of robots is becoming more important than ever before. Robotic arm joint motor-drive systems are vulnerable to hardware failures due to harsh operating environment in many scenarios, which may yield various joint failures and result in significant downtime costs. Targeting the most common robotic joint brushless DC (BLDC) motor-drive systems, this paper proposes a robust online diagnostic method for semiconductor faults for BLDC motor drives. The proposed fault diagnostic technique is based on the stator current signature analysis. Specifically, this paper investigates the performance of the BLDC joint motors under open-circuit faults of the inverter switches using finite element co-simulation tools. Furthermore, the proposed methodology is not only capable of detecting any open-circuit faults but also identifying faulty switches based on a knowledge table by considering various fault conditions. The robustness of the proposed technique was verified through extensive simulations under different speed and load conditions. Moreover, simulations have been carried out on a Kinova Gen-3 robot arm to verify the theoretical findings, highlighting the impacts of locked joints on the robot’s end-effector locations. Finally, experimental results are presented to corroborate the performance of the proposed fault diagnostic strategy. Full article
(This article belongs to the Special Issue Towards Electric Motors and Drives: Condition Monitoring, Performance Prediction and Fault Diagnosis)
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13 pages, 5419 KiB  
Article
Design and Test of a 2-DOF Compliant Positioning Stage with Antagonistic Piezoelectric Actuation
by Haitao Wu, Hui Tang and Yanding Qin
Machines 2024, 12(6), 420; https://doi.org/10.3390/machines12060420 - 19 Jun 2024
Cited by 4 | Viewed by 1282
Abstract
This paper designs a two-degrees-of-freedom (DOF) compliant positioning stage with antagonistic piezoelectric actuation. Two pairs of PEAs are arranged in an antagonistic configuration to generate reciprocating motions. Flexure mechanisms are intentionally adopted to construct the fixtures for PEAs, whose elastic deformations can help [...] Read more.
This paper designs a two-degrees-of-freedom (DOF) compliant positioning stage with antagonistic piezoelectric actuation. Two pairs of PEAs are arranged in an antagonistic configuration to generate reciprocating motions. Flexure mechanisms are intentionally adopted to construct the fixtures for PEAs, whose elastic deformations can help to reduce the stress concentration on the PEA caused by the extension of the PEA in the other direction. Subsequently, the parameter and performance of the 2-DOF compliant positioning stage is optimized and verified by finite element analysis. Finally, a prototype is fabricated and tested. The experimental results show that the developed positioning stage achieves a working stroke of 28.27 μm × 27.62 μm. Motion resolutions of both axes are 8 nm and natural frequencies in the working directions are up to 2018 Hz, which is promising for high-precision positioning control. Full article
(This article belongs to the Special Issue Optimization and Design of Compliant Mechanisms)
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20 pages, 11034 KiB  
Article
Experimental Evaluation of Mechanical Compression Properties of Aluminum Alloy Lattice Trusses for Anti-Ice System Applications
by Carlo Giovanni Ferro, Sara Varetti and Paolo Maggiore
Machines 2024, 12(6), 404; https://doi.org/10.3390/machines12060404 - 12 Jun 2024
Viewed by 1136
Abstract
Lattice structures have emerged as promising materials for aerospace structure applications due to their high strength-to-weight ratios, customizable properties, and efficient use of materials. These properties make them attractive for use in anti-ice systems, where lightweight and heat exchange are essential. This paper [...] Read more.
Lattice structures have emerged as promising materials for aerospace structure applications due to their high strength-to-weight ratios, customizable properties, and efficient use of materials. These properties make them attractive for use in anti-ice systems, where lightweight and heat exchange are essential. This paper presents an extensive experimental investigation into mechanical compression properties of lattice trusses fabricated from AlSi10Mg powder alloy, a material commonly used in casted aerospace parts. The truss structures were manufactured using the additive manufacturing selective laser melting technique and were subjected to uniaxial compressive loading to assess their performance. The results demonstrate that AlSi10Mg lattice trusses exhibit remarkable compressive strength with strong correlations depending upon both topology and cells’ parameters setup. The findings described highlight the potential of AlSi10Mg alloy as a promising material for custom truss fabrication, offering customizable cost-effective and lightweight solutions for the aerospace market. This study also emphasizes the role of additive manufacturing in producing complex structures with pointwise-tailored mechanical properties. Full article
(This article belongs to the Special Issue Recent Advances in 3D Printing in Industry 4.0)
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