Machines

15 pages, 3090 KiB

Open AccessArticle

Diagnosing Faults of Pneumatic Soft Actuators Based on Multimodal Spatiotemporal Features and Ensemble Learning

by Tao Duan, Yi Lv, Liyuan Wang, Haifan Li, Teng Yi, Yigang He and Zhongming Lv

Machines 2025, 13(8), 749; https://doi.org/10.3390/machines13080749 - 21 Aug 2025

Viewed by 91

Soft robots demonstrate significant advantages in applications within complex environments due to their unique material properties and structural designs. However, they also face challenges in fault diagnosis, such as nonlinearity, time variability, and the difficulty of precise modeling. To address these issues, this [...] Read more.

Soft robots demonstrate significant advantages in applications within complex environments due to their unique material properties and structural designs. However, they also face challenges in fault diagnosis, such as nonlinearity, time variability, and the difficulty of precise modeling. To address these issues, this paper proposes a fault diagnosis method based on multimodal spatiotemporal features and ensemble learning. First, a sliding-window Kalman filter is utilized to eliminate noise interference from multi-source signals, constructing separate temporal and spatial representation spaces. Subsequently, an adaptive weight strategy for feature fusion is applied to train a heterogeneous decision tree model, followed by a dynamic weighted voting mechanism based on confidence levels to obtain diagnostic results. This method optimizes the feature extraction and fusion process in stages, combined with a dynamic ensemble strategy. Experimental results indicate a significant improvement in diagnostic accuracy and model robustness, achieving precise identification of faults in soft robots. Full article

(This article belongs to the Section Machines Testing and Maintenance)

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21 pages, 3286 KiB

Open AccessArticle

ELM-GA-Based Active Comfort Control of a Piggyback Transfer Robot

by Liyan Feng, Xinping Wang, Teng Liu, Kaicheng Qi, Long Zhang, Jianjun Zhang and Shijie Guo

Machines 2025, 13(8), 748; https://doi.org/10.3390/machines13080748 - 21 Aug 2025

Viewed by 129

Abstract

The improvement of comfort in the human–robot interaction for care recipients is a significant challenge in the development of nursing robots. The existing methods for enhancing comfort largely depend on subjective comfort questionnaires, which are prone to unavoidable errors. Additionally, traditional passive movement [...] Read more.

The improvement of comfort in the human–robot interaction for care recipients is a significant challenge in the development of nursing robots. The existing methods for enhancing comfort largely depend on subjective comfort questionnaires, which are prone to unavoidable errors. Additionally, traditional passive movement control approaches lack the ability to adapt and effectively improve care recipient comfort. To address these problems, this paper proposes an active, personalized intelligent control method based on neural networks. A muscle activation prediction model is established for the piggyback transfer robot, enabling dynamic adjustments during the care process to improve human comfort. Initially, a kinematic analysis of the piggyback transfer robot is conducted to determine the optimal back-carrying trajectory. Experiments were carried out to measure human–robot contact forces, chest holder rotation angles, and muscle activation levels. Subsequently, an Online Sequential Extreme Learning Machine (OS-ELM) algorithm is used to train a predictive model. The model takes the contact forces and chest holder rotation angle as inputs, while outputting the latissimus dorsi muscle activation levels. The Genetic Algorithm (GA) is then employed to dynamically adjust the chest holder’s rotation angle to minimize the difference between actual muscle activation and the comfort threshold. Comparative experiments demonstrate that the proposed ELM-GA-based active control method effectively enhances comfort during the piggyback transfer process, as evidenced by both subjective feedback and objective measurements of muscle activation. Full article

(This article belongs to the Special Issue Vibration Isolation and Control in Mechanical Systems)

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18 pages, 7200 KiB

Open AccessArticle

Dynamic Characteristic Analysis and Experimental Verification of Rotor Systems in Large Synchronous Motors

by Yushuai Liu, Jiahao Hou, Rui Li and Qingshun Bai

Machines 2025, 13(8), 747; https://doi.org/10.3390/machines13080747 - 21 Aug 2025

Viewed by 141

Abstract

Large synchronous motors are typically used to drive various load equipment, such as reciprocating compressors. Due to the continuous oscillation of the load, the pulsating torque acting on the main shaft of the synchronous motor will continuously vary with the load changes. This [...] Read more.

Large synchronous motors are typically used to drive various load equipment, such as reciprocating compressors. Due to the continuous oscillation of the load, the pulsating torque acting on the main shaft of the synchronous motor will continuously vary with the load changes. This leads to forced oscillations during the dynamic stable operation of the unit, subsequently causing severe problems such as overheating, noise, and failures. Moreover, the rotor length of large synchronous motors is generally greater than the rotor diameter, giving the rotor certain flexible characteristics. During a motor’s operation, it is necessary to cross the first-order critical speed, making resonance highly likely to occur. Therefore, the analysis of dynamic characteristics of large synchronous motors is particularly important. This study investigates the dynamic characteristics of a 7800 kW-18P large synchronous motor rotor system through comprehensive theoretical and experimental analyses. The research encompasses three key aspects: (1) modal analysis comparing fan-equipped and fan-free configurations, (2) harmonic response evaluation, and (3) critical speed determination under concentrated mass conditions. Experimental validation was performed via impact hammer testing, with measured natural frequencies showing a strong correlation with simulated results for the magnetic pole core assembly. The findings not only confirm the operational speed validity but also establish a reliable foundation for the subsequent structural optimization of high-power synchronous machines. Full article

(This article belongs to the Special Issue Electrical Machines: Design, Modeling and Control)

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18 pages, 2590 KiB

Open AccessArticle

Use of Artificial Neural Networks and SCADA Data for Early Detection of Wind Turbine Gearbox Failures

by Bryan Puruncajas, Francesco Castellani, Yolanda Vidal and Christian Tutivén

Machines 2025, 13(8), 746; https://doi.org/10.3390/machines13080746 - 20 Aug 2025

Viewed by 120

Abstract

This paper investigates the utilization of artificial neural networks (ANNs) for the proactive identification of gearbox failures in wind turbines, boosting the use of operational SCADA data for predictive analysis. Avoiding gearbox failures, which can strongly impact the functioning of wind turbines, is [...] Read more.

This paper investigates the utilization of artificial neural networks (ANNs) for the proactive identification of gearbox failures in wind turbines, boosting the use of operational SCADA data for predictive analysis. Avoiding gearbox failures, which can strongly impact the functioning of wind turbines, is crucial for ensuring high reliability and efficiency within wind farms. Early detection can be achieved though the development of a normal behavior model based on ANNs, which are trained with data from healthy conditions derived from selected SCADA variables that are closely associated with gearbox operations. The objective of this model is to forecast deviations in the gear bearing temperature, which serve as an early warning alert for potential failures. The research employs extensive SCADA data collected from January 2018 to February 2022 from a wind farm with multiple turbines. The study guarantees the robustness of the model through a thorough data cleaning process, normalization, and splitting into training, validation, and testing sets. The findings reveal that the model is able to effectively identify anomalies in gear bearing temperatures several months prior to failure, outperforming simple data processing methods, thereby offering a significant lead time for maintenance actions. This early detection capability is highlighted by a case study involving a gearbox failure in one of the turbines, where the proposed ANN model detected the issue months ahead of the actual failure. The present paper is an extended version of the work presented at the 5th International Conference of IFToMM ITALY 2024. Full article

(This article belongs to the Special Issue Advances in Mechanism and Machine Science Within the IFIT 2024 Conference)

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31 pages, 14150 KiB

Open AccessArticle

A Development Method for Load Adaptive Matching Digital Twin System of Bridge Cranes

by Junqi Li, Qing Dong, Gening Xu, Yifan Zuo and Lili Jiang

Machines 2025, 13(8), 745; https://doi.org/10.3390/machines13080745 - 20 Aug 2025

Viewed by 75

Abstract

Bridge cranes generally have a significant disparity between their actual service life and design life. If they are scrapped according to the design life, it is likely to result in resource wastage or pose potential safety hazards due to extended service. Existing studies [...] Read more.

Bridge cranes generally have a significant disparity between their actual service life and design life. If they are scrapped according to the design life, it is likely to result in resource wastage or pose potential safety hazards due to extended service. Existing studies have not thoroughly examined the coupling relationship among actual working conditions, structural damage, and load-matching strategies. It is difficult to achieve real-time and accurate adaptation between loads and the carrying capacity of equipment, and thus cannot effectively narrow this life gap. To this end, this paper defines a digital twin system framework for crane load adaptive matching, constructs a load adaptive matching optimization model, proposes a method for developing a digital twin system for bridge crane load adaptive matching, and builds a digital twin system platform centered on virtual-real mapping, IoT connectivity, and data interaction. Detailed experimental verification was conducted using the DQ40 kg-1.8 m-1.3 m light-duty bridge crane. The results demonstrate that this method and system can effectively achieve dynamic matching between the load and real-time carrying capacity. While ensuring the service life exceeds the design life, the difference between the two is controlled at around 3467 cycles, accounting for approximately 0.000462% of the design life. This significantly improves the equipment’s operational safety and resource utilization efficiency, breaks through the limitations of load reduction schemes formulated based on human experience under the traditional regular inspection mode, and provides a scientific load-matching decision-making basis and technical support for special equipment inspection institutions and users. Full article

(This article belongs to the Section Automation and Control Systems)

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32 pages, 2063 KiB

Open AccessArticle

Multi-Environmental Reliability Evaluation for Complex Equipment: A Strict Intuitionistic Fuzzy Distance Measure-Based Multi-Attribute Group Decision-Making Framework

by Zhaiming Peng, Wenhe Chen and Longlong Gao

Machines 2025, 13(8), 744; https://doi.org/10.3390/machines13080744 - 20 Aug 2025

Viewed by 84

Abstract

The theoretical reliability of complex equipment often significantly deviates from real-world performance due to the inherent influence of diverse environmental and operational factors, making scientific reliability evaluation particularly challenging. This study proposes a multi-attribute group decision-making (MAGDM) evaluation framework based on a strict [...] Read more.

The theoretical reliability of complex equipment often significantly deviates from real-world performance due to the inherent influence of diverse environmental and operational factors, making scientific reliability evaluation particularly challenging. This study proposes a multi-attribute group decision-making (MAGDM) evaluation framework based on a strict intuitionistic fuzzy distance and an improved TOPSIS approach. First, an improved strict intuitionistic fuzzy distance measure (ISIFDisM) is rigorously developed to overcome the limitations of existing methods, exhibiting high robustness, monotonicity, and discriminability. Second, building upon ISIFDisM, a systematic MAGDM evaluation model is constructed, comprising three key steps: (1) data acquisition through structured questionnaire surveys; (2) attribute weights determined using the entropy weight method; and (3) alternative ranking through normalized priority coefficients derived from intuitionistic fuzzy distance calculations. Third, the proposed framework is applied to a practical case study focused on reliability assessment of ship equipment, enabling effective ranking of various marine engines. Finally, through static comparative analyses and dynamic scenario simulations, the feasibility, robustness, and methodological superiority of the proposed framework are thoroughly validated. Full article

(This article belongs to the Special Issue Reliability Analysis and Fault Diagnosis of Safety-Critical Systems Using Data-Driven Approaches)

1 pages, 127 KiB

Open AccessCorrection

Correction: Kaviani, H.R.; Moshfeghi, M. Power Generation Enhancement of Horizontal Axis Wind Turbines Using Bioinspired Airfoils: A CFD Study. Machines 2023, 11, 998

by Hamid R. Kaviani and Mohammad Moshfeghi

Machines 2025, 13(8), 743; https://doi.org/10.3390/machines13080743 - 20 Aug 2025

Viewed by 67

Abstract

There was an error in the original publication [...] Full article

(This article belongs to the Special Issue Recent Developments in Offshore Wind Turbines)

24 pages, 4431 KiB

Open AccessArticle

Fault Classification in Power Transformers Using Dissolved Gas Analysis and Optimized Machine Learning Algorithms

by Vuyani M. N. Dladla and Bonginkosi A. Thango

Machines 2025, 13(8), 742; https://doi.org/10.3390/machines13080742 - 20 Aug 2025

Viewed by 161

Abstract

Power transformers are critical assets in electrical power systems, yet their fault diagnosis often relies on conventional dissolved gas analysis (DGA) methods such as the Duval Pentagon and Triangle, Key Gas, and Rogers Ratio methods. Even though these methods are commonly used, they [...] Read more.

Power transformers are critical assets in electrical power systems, yet their fault diagnosis often relies on conventional dissolved gas analysis (DGA) methods such as the Duval Pentagon and Triangle, Key Gas, and Rogers Ratio methods. Even though these methods are commonly used, they present limitations in classification accuracy, concurrent fault identification, and manual sample handling. In this study, a framework of optimized machine learning algorithms that integrates Chi-squared statistical feature selection with Random Search hyperparameter optimization algorithms was developed to enhance transformer fault classification accuracy using DGA data, thereby addressing the limitations of conventional methods and improving diagnostic precision. Utilizing the R2024b MATLAB Classification Learner App, five optimized machine learning algorithms were trained and tested using 282 transformer oil samples with varying DGA gas concentrations obtained from industrial transformers, the IEC TC10 database, and the literature. The optimized and assessed models are Linear Discriminant, Naïve Bayes, Decision Trees, Support Vector Machine, Neural Networks, k-Nearest Neighbor, and the Ensemble Algorithm. From the proposed models, the best performing algorithm, Optimized k-Nearest Neighbor, achieved an overall performance accuracy of 92.478%, followed by the Optimized Neural Network at 89.823%. To assess their performance against the conventional methods, the same dataset used for the optimized machine learning algorithms was used to evaluate the performance of the Duval Triangle and Duval Pentagon methods using VAISALA DGA software version 1.1.0; the proposed models outperformed the conventional methods, which could only achieve a classification accuracy of 35.757% and 30.818%, respectively. This study concludes that the application of the proposed optimized machine learning algorithms can enhance the classification accuracy of DGA-based faults in power transformers, supporting more reliable diagnostics and proactive maintenance strategies. Full article

(This article belongs to the Section Electrical Machines and Drives)

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14 pages, 3670 KiB

Open AccessReview

Historical Evolution of Heavy Machinery and a General Role of Multibody Dynamics

by Suraj Jaiswal and Mohammad Poursina

Machines 2025, 13(8), 741; https://doi.org/10.3390/machines13080741 - 20 Aug 2025

Viewed by 298

Abstract

Heavy machinery has evolved significantly in productivity, efficiency, and safety over the past century and a half, where most technological advances occurred after the 1990s. The field of multibody dynamics has significantly contributed to this development. The objective of this study is to [...] Read more.

Heavy machinery has evolved significantly in productivity, efficiency, and safety over the past century and a half, where most technological advances occurred after the 1990s. The field of multibody dynamics has significantly contributed to this development. The objective of this study is to introduce the historical evolution of heavy machinery and to analyze the general role of multibody dynamics in this evolution. The latter part of the objective is the novel contribution of this study. The historical evolution of heavy machinery is presented from the ancient times of 30–20 BC to the modern innovations of 2024. The general role of multibody dynamics in heavy machinery is identified and analyzed in five phases, from using simple kinematic models in the 1970s to real-time simulations and autonomous systems in the 2020s. This study can serve as a benchmark for future work. Full article

(This article belongs to the Section Machine Design and Theory)

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20 pages, 5937 KiB

Open AccessArticle

Stator Fault Diagnostics in Asymmetrical Six-Phase Induction Motor Drives with Model Predictive Control Applicable During Transient Speeds

by Hugo R. P. Antunes, Davide. S. B. Fonseca, João Serra and Antonio J. Marques Cardoso

Machines 2025, 13(8), 740; https://doi.org/10.3390/machines13080740 - 19 Aug 2025

Viewed by 132

Abstract

Abrupt speed variations and motor start-ups have been pointed out as critical challenges in the framework of fault diagnostics in induction motor drives, namely inter-turn short circuit faults. Generally, abrupt accelerations influence the typical symptoms of the fault, and consequently, the fault detection [...] Read more.

Abrupt speed variations and motor start-ups have been pointed out as critical challenges in the framework of fault diagnostics in induction motor drives, namely inter-turn short circuit faults. Generally, abrupt accelerations influence the typical symptoms of the fault, and consequently, the fault detection becomes ambiguous, impacting prompt and effective decision-making. To overcome this issue, this study proposes an inter-turn short-circuit fault diagnostic technique for asymmetrical six-phase induction motor drives operating under both smooth and abrupt motor accelerations. A time–frequency domain spectrogram of the AC component extracted from the q-axis reference current signal serves as a reliable fault indicator. This technique stands out for the compromise between robustness and computational effort using only one control variable accessible in the model predictive control algorithm, thus discarding both voltage and current signals. Experimental tests involving various load torques and fault severities, in transient regimes, were performed to validate the proposed methodology’s effectiveness thoroughly. Full article

(This article belongs to the Section Electrical Machines and Drives)

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18 pages, 4526 KiB

Open AccessArticle

To Enhance the Aerodynamic Power Efficiency of Vertical Axis Wind Turbines: Proposing Morphing Strategies for Variable Wind Speed

by Hanif Ullah, Yang Huang, Vincenzo Gulizzi and Antonio Pantano

Machines 2025, 13(8), 739; https://doi.org/10.3390/machines13080739 - 19 Aug 2025

Viewed by 177

Abstract

This study investigates the aerodynamic performance of vertical axis wind turbines (VAWTs), focusing on a novel dual-airfoil morphing mechanism for H-type Darrieus turbines. By leveraging the aerodynamic benefits of two distinct airfoil profiles, the proposed design adapts dynamically to varying wind speeds, enhancing [...] Read more.

This study investigates the aerodynamic performance of vertical axis wind turbines (VAWTs), focusing on a novel dual-airfoil morphing mechanism for H-type Darrieus turbines. By leveraging the aerodynamic benefits of two distinct airfoil profiles, the proposed design adapts dynamically to varying wind speeds, enhancing overall efficiency. The methodology includes airfoil selection and aerodynamic analysis using the Double Multiple Stream Tube (DMST) model, simulated in QBlade software. The numerical model was validated against established benchmark data, confirming its accuracy. Key findings reveal that among all tested airfoils, the NACA 64(2)-415 airfoil achieves the highest power coefficient at low wind speeds, while the FX 84-W-127 airfoil performs optimally at higher wind speeds. Inspired by biomimetic principles, a morphing strategy and mechanism is proposed to transition seamlessly between these two profiles and enable broader operational adaptability. This innovative approach demonstrates significant potential for improving the energy capture efficiency and viability of VAWTs, contributing to the advancement of renewable wind energy technologies. Full article

(This article belongs to the Special Issue Mechanical Design and Aerodynamic Optimization of Wind Turbine Components)

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18 pages, 6922 KiB

Open AccessArticle

Compact Liquid Cooling Garment with Integrated Vapor Compression Refrigeration for Extreme High-Temperature Environments

by Yuancheng Zhu, Yonghong He and Weiguo Xiong

Machines 2025, 13(8), 738; https://doi.org/10.3390/machines13080738 - 19 Aug 2025

Viewed by 182

Abstract

Extreme high-temperature environments pose challenges for human thermal comfort and safety. This study introduces a compact portable liquid cooling garment weighing 3.6 kg in total with an integrated 1.99 kg vapor compression refrigeration unit (172 mm × 80 mm × 130 mm). This [...] Read more.

Extreme high-temperature environments pose challenges for human thermal comfort and safety. This study introduces a compact portable liquid cooling garment weighing 3.6 kg in total with an integrated 1.99 kg vapor compression refrigeration unit (172 mm × 80 mm × 130 mm). This system innovatively integrates a patented evaporator-pump module and an optimized miniature rotary compressor, achieving a 151 W cooling capacity at 55 °C ambient temperature, surpassing existing portable systems in compactness and performance. Human trials with eight male participants at 35 °C (walking) and 40 °C (sitting) demonstrated that the liquid cooling garment system significantly improved thermal comfort. The mean thermal comfort vote decreased from 2.63 (uncomfortable) to 1.13 (slightly uncomfortable) while walking and from 3.88 (very uncomfortable) to 1.25 (slightly uncomfortable) while sitting. The mean skin temperature in the final stable state was reduced by 0.34 °C in walking trials and 1.09 °C in sitting trials, and heart rate decreased by up to 10.2 bpm in sedentary conditions. Comprehensive human trials under extreme heat further validate this system’s efficacy. This lightweight, efficient system offers a practical solution for personal thermal management in extreme high-temperature environments, with potential applications in industrial safety, military operations, and emergency response. Full article

(This article belongs to the Section Turbomachinery)

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21 pages, 4332 KiB

Open AccessArticle

A Comparative Study of Time–Frequency Representations for Bearing and Rotating Fault Diagnosis Using Vision Transformer

by Ahmet Orhan, Nikolay Yordanov, Merve Ertarğın, Marin Zhilevski and Mikho Mikhov

Machines 2025, 13(8), 737; https://doi.org/10.3390/machines13080737 - 19 Aug 2025

Viewed by 356

Abstract

This paper presents a comparative analysis of bearing and rotating component fault classification based on different time–frequency representations using vision transformer (ViT). Four different time–frequency transformation techniques—short-time Fourier transform (STFT), continuous wavelet transform (CWT), Hilbert–Huang transform (HHT), and Wigner–Ville distribution (WVD)—were applied to [...] Read more.

This paper presents a comparative analysis of bearing and rotating component fault classification based on different time–frequency representations using vision transformer (ViT). Four different time–frequency transformation techniques—short-time Fourier transform (STFT), continuous wavelet transform (CWT), Hilbert–Huang transform (HHT), and Wigner–Ville distribution (WVD)—were applied to convert the signals into 2D images. A pretrained ViT-Base architecture was fine-tuned on the resulting images for classification tasks. The model was evaluated on two separate scenarios: (i) eight-class rotating component fault classification and (ii) four-class bearing fault classification. Importantly, in each task, the samples were collected under varying conditions of the other component (i.e., different rotating conditions in bearing classification and vice versa). This design allowed for an independent assessment of the model’s ability to generalize across fault domains. The experimental results demonstrate that the ViT-based approach achieves high classification performance across various time–frequency representations, highlighting its potential for mechanical fault diagnosis in rotating machinery. Notably, the model achieved higher accuracy in bearing fault classification compared to rotating component faults, suggesting higher sensitivity to bearing-related anomalies. Full article

(This article belongs to the Section Machines Testing and Maintenance)

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22 pages, 10787 KiB

Open AccessArticle

Multi-Source Error Coupling and Tolerance Optimization for Improving the Precision of Automated Assembly of Aircraft Components

by Tailong Cao, Xiang Huang, Shuanggao Li and Guoyi Hou

Machines 2025, 13(8), 736; https://doi.org/10.3390/machines13080736 - 19 Aug 2025

Viewed by 372

Abstract

In automated aircraft assembly, achieving high-precision alignment is essential due to the presence of multiple coupled error sources that significantly affect final product quality. This study proposes an integrated framework to model multi-source errors via a directed coupling network and to quantify their [...] Read more.

In automated aircraft assembly, achieving high-precision alignment is essential due to the presence of multiple coupled error sources that significantly affect final product quality. This study proposes an integrated framework to model multi-source errors via a directed coupling network and to quantify their impact using Monte Carlo simulations. To reduce the complexity of tolerance allocation, Sobol-based global sensitivity analysis is applied to identify dominant contributors to assembly deviations. The most influential parameters are retained for multi-objective optimization using the non-dominated sorting genetic algorithm II (NSGA-II). This framework enables the minimization of key assembly deviations while maintaining computational efficiency. Experimental validation on a typical helicopter ring assembly demonstrates that the proposed optimization approach increases the position pass rate from 67.4% to 100.0% and the coaxiality pass rate from 93.5% to 100.0%. The corresponding process capability indices (CPK) also improve significantly, from 0.31 to 2.19 for position and from 0.62 to 1.06 for coaxiality. These improvements not only satisfy high-precision assembly requirements but also exceed common industry benchmarks, demonstrating the method’s practical effectiveness under multi-source uncertainty. Full article

(This article belongs to the Section Automation and Control Systems)

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24 pages, 6883 KiB

Open AccessArticle

A Human-in-the-Loop Study of Eye-Movement-Based Control for Workload Reduction in Delayed Teleoperation of Ground Vehicles

by Qiang Zhang, Aiping Zhao, Feng Zhao and Wangyu Wu

Machines 2025, 13(8), 735; https://doi.org/10.3390/machines13080735 - 18 Aug 2025

Viewed by 306

Abstract

Teleoperated ground vehicles (TGVs) are widely applied in hazardous and dynamic environments, where communication delay and low transparency increase operator workload and reduce control performance. This study explores the cognitive and physiological workload associated with such conditions and evaluates the effectiveness of an [...] Read more.

Teleoperated ground vehicles (TGVs) are widely applied in hazardous and dynamic environments, where communication delay and low transparency increase operator workload and reduce control performance. This study explores the cognitive and physiological workload associated with such conditions and evaluates the effectiveness of an eye-movement-based predicted trajectory guidance control (ePTGC) framework in alleviating operator burden. A human-in-the-loop teleoperation experiment was conducted using a 2 × 2 within-subject design, incorporating subjective ratings (NASA-TLX), objective performance metrics from a dual-task paradigm (one-back memory task), and multimodal physiological indicators (ECG and EDA). Results show that delay and low transparency significantly elevated subjective, objective, and physiological workload levels. Compared to direct control (DC), the ePTGC framework significantly reduced workload across all three dimensions, particularly under high-delay conditions, while maintaining or even improving task performance. Notably, ePTGC enabled even lower workload levels under low-delay conditions than the baseline condition. These findings demonstrate the potential of the ePTGC framework to enhance teleoperation stability and reduce operator burden in delay-prone and low-transparency scenarios. Full article

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17 pages, 1917 KiB

Open AccessArticle

Lyapunov-Based Adaptive Sliding Mode Control of DC–DC Boost Converters Under Parametric Uncertainties

by Hamza Sahraoui, Hacene Mellah, Souhil Mouassa, Francisco Jurado and Taieb Bessaad

Machines 2025, 13(8), 734; https://doi.org/10.3390/machines13080734 - 18 Aug 2025

Viewed by 267

Abstract

The increasing demand for high-performance power converters for electric vehicle (EV) applications places a significant emphasis on developing effective and robust control strategies for DC-DC converter operation. This paper deals with the development, simulation, and experimental validation of an adaptive Lyapunov-type Nonlinear Sliding [...] Read more.

The increasing demand for high-performance power converters for electric vehicle (EV) applications places a significant emphasis on developing effective and robust control strategies for DC-DC converter operation. This paper deals with the development, simulation, and experimental validation of an adaptive Lyapunov-type Nonlinear Sliding Mode Control (L-SMC) strategy for a DC–DC boost converter, addressing significant uncertainties caused by large variations in system parameters (R and L) and ensuring the tracking of a voltage reference. The proposed control strategy employs the Lyapunov stability theory to build an adaptive law to update the parameters of the sliding surface so the system can achieve global asymptotic stability in the presence of uncertainty in inductance, capacitance, load resistance, and input voltage. The nonlinear sliding manifold is also considered, which contributes to a more robust and faster convergence in the controller. In addition, a logic optimization technique was implemented that minimizes switching (chattering) operations significantly, and as a result of this, increases ease of implementation. The proposed L-SMC is validated through both simulation and experimental tests under various conditions, including abrupt increases in input voltage and load disturbances. Simulation results demonstrate that, whether under nominal parameters (R = 320 Ω, L = 2.7 mH) or with parameter variations, the voltage overshoot in all cases remains below 0.5%, while the steady-state error stays under 0.4 V except during the startup, which is a transitional phase lasting a very short time. The current responds smoothly to voltage reference and parameter variations, with very insignificant chattering and overshoot. The current remains stable and constant, with a noticeable presence of a peak with each change in the reference voltage, accompanied by relatively small chattering. The simulation and experimental results demonstrate that adaptive L-SMC achieves accurate voltage regulation, a rapid transient response, and reduces chattering, and the simulation and experimental testing show that the proposed controller has a significantly lower steady-state error, which ensures precise and stable voltage regulation with time. Additionally, the system converges faster for the proposed controller at conversion and is stabilized quickly to the adaptation reference state after the drastic and dynamic change in either the input voltage or load, thus minimizing the settling time. The proposed control approach also contributes to saving energy for the application at hand, all in consideration of minimizing losses. Full article

(This article belongs to the Special Issue Advanced Protection and Control Strategies for Power Conversion in Electromechanical Energy Storage Systems)

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18 pages, 3836 KiB

Open AccessArticle

Investigation of Blade Root Clearance Flow Effects on Pressure Fluctuations in an Axial Flow Pump

by Fan Meng, Yanjun Li, Mingzhe Li and Chao Ning

Machines 2025, 13(8), 733; https://doi.org/10.3390/machines13080733 - 18 Aug 2025

Viewed by 241

Abstract

This study investigates the leakage vortex influence on pressure pulsation characteristics within a vertical axial flow pump. Three impeller configurations with blade root clearance (δ) of 2.7–8.0 mm were designed to analyze geometric effects on internal flow dynamics. Unsteady RANS simulations [...] Read more.

This study investigates the leakage vortex influence on pressure pulsation characteristics within a vertical axial flow pump. Three impeller configurations with blade root clearance (δ) of 2.7–8.0 mm were designed to analyze geometric effects on internal flow dynamics. Unsteady RANS simulations predicted flow structures under multiple operating conditions (0.8–1.2Q_des). Fast Fourier Transform (FFT) extracted frequency–domain and time–frequency characteristics of pressure pulsations in critical flow regions. Key results reveal: (1) δ enlargement expands low-pressure zones within blade channels due to enhanced leakage vortices; (2) leading-edge pulsation shows 8.2–11.7% reduction in peak-to-peak amplitude and fundamental frequency magnitude with increasing δ; (3) trailing-edge response exhibits non-monotonic behavior, with maximum amplitude at δ = 5.0 mm (42.2% increase at design flow). These findings demonstrate that blade root clearance optimization requires condition-dependent thresholds to balance leakage management and pulsation control. Full article

(This article belongs to the Special Issue Advanced Research and Development in Fluid Machinery: Design, Optimization, and Applications)

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22 pages, 5085 KiB

Open AccessArticle

Energy-Efficient Scheduling in Heat Treatment Workshops Based on Task Clustering and Job Batching

by Dapeng Su, Tianyi Zhang, Siyang Ji and Jihong Yan

Machines 2025, 13(8), 732; https://doi.org/10.3390/machines13080732 - 18 Aug 2025

Viewed by 249

Abstract

The development of green and efficient manufacturing has brought on complex trade-offs between energy consumption control and resource utilization efficiency in heat treatment tasks. Traditional single-piece scheduling methods are challenged in addressing the complexity of multiple tasks and energy optimization. In this paper, [...] Read more.

The development of green and efficient manufacturing has brought on complex trade-offs between energy consumption control and resource utilization efficiency in heat treatment tasks. Traditional single-piece scheduling methods are challenged in addressing the complexity of multiple tasks and energy optimization. In this paper, an optimized scheduling method for heat treatment workshops is proposed by integrating task grouping and batch combination strategies. Specifically, a genetic algorithm enhanced with local search and adaptive mutation operators is proposed under constraints such as delivery deadlines and equipment capacity. During the strategy generation process, equipment changeover and idle time are considered. By performing multi-dimensional matching of workpiece processing processes, heat treatment requirements, and quality characteristics, an innovative clustering mechanism for dynamic production batches based on task similarity is constructed. To validate the effectiveness, actual production data from a heat treatment workshop were selected for analysis and evaluation. The results show that the proposed method reduces the total production time by 31.6% with on-time delivery of orders, and the equipment operation frequency is reduced by 28.4%, which verifies the practicality and advancement of the proposed method. Full article

(This article belongs to the Topic Smart Product Design and Manufacturing on Industrial Internet)

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28 pages, 1534 KiB

Open AccessArticle

Trust-Based Modular Cyber–Physical–Human Robotic System for Collaborative Manufacturing: Modulating Communications

by S. M. Mizanoor Rahman

Machines 2025, 13(8), 731; https://doi.org/10.3390/machines13080731 - 17 Aug 2025

Viewed by 207

Abstract

The objective was to propose a human–robot bidirectional trust-triggered cyber–physical–human (CPH) system framework for human–robot collaborative assembly in flexible manufacturing and investigate the impact of modulating communications in the CPH system on system performance and human–robot interactions (HRIs). As the research method, we [...] Read more.

The objective was to propose a human–robot bidirectional trust-triggered cyber–physical–human (CPH) system framework for human–robot collaborative assembly in flexible manufacturing and investigate the impact of modulating communications in the CPH system on system performance and human–robot interactions (HRIs). As the research method, we developed a one human–one robot hybrid cell where a human and a robot collaborated with each other to perform the assembly operation of different manufacturing components in a flexible manufacturing setup. We configured the human–robot collaborative system in three interconnected components of a CPH system: (i) cyber system, (ii) physical system, and (iii) human system. We divided the functions of the CPH system into three interconnected modules: (i) communication, (ii) computing or computation, and (iii) control. We derived a model to compute the human and robot’s bidirectional trust in each other in real time. We implemented the trust-triggered CPH framework on the human–robot collaborative assembly setup and modulated the communication methods among the cyber, physical, and human components of the CPH system in different innovative ways in three separate experiments. The research results show that modulating the communication methods triggered by bidirectional trust impacts on the effectiveness of the CPH system in terms of human–robot interactions, and task performance (efficiency and quality) differently. The results show that communication methods with an appropriate combination of a higher number of communication modes (cues) produces better HRIs and task performance. Based on a comparative study, it was concluded that the results prove the efficacy and superiority of configuring the HRC system in the form of a modular CPH system over using conventional HRC systems in terms of HRI and task performance. Configuring human–robot collaborative systems in the form of a CPH system can transform the design, development, analysis, and control of the systems and enhance their scope, ease, and effectiveness for various applications, such as industrial manufacturing, construction, transport and logistics, forestry, etc. Full article

(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)

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19 pages, 9297 KiB

Open AccessArticle

Vibration Control of Wheels in Distributed Drive Electric Vehicle Based on Electro-Mechanical Braking

by Yinggang Xu, Zheng Zhu, Zhaonan Li, Xiangyu Wang, Liang Li and Heng Wei

Machines 2025, 13(8), 730; https://doi.org/10.3390/machines13080730 - 17 Aug 2025

Viewed by 268

Abstract

Electro-Mechanical Braking (EMB), as a novel brake-by-wire technology, is rapidly being implemented in vehicle chassis systems. Nevertheless, the integrated design of the EMB caliper contributes to an increased unsprung mass in Distributed Drive Electric Vehicles (DDEVs). Experimental results indicate that when the Anti-lock [...] Read more.

Electro-Mechanical Braking (EMB), as a novel brake-by-wire technology, is rapidly being implemented in vehicle chassis systems. Nevertheless, the integrated design of the EMB caliper contributes to an increased unsprung mass in Distributed Drive Electric Vehicles (DDEVs). Experimental results indicate that when the Anti-lock Braking System (ABS) is activated, these factors can induce high-frequency wheel oscillations. To address this issue, this study proposes an anti-oscillation control strategy tailored for EMB systems. Firstly, a quarter-vehicle model is established that incorporates the dynamics of the drive motor, suspension, and tire, enabling analysis of the system’s resonant behavior. The Discrete Fourier Transform (DFT) is applied to the difference between wheel speed and vehicle speed to extract the dominant frequency components. Then, an Adaptive Braking Intensity Field Regulation (ABIFR) strategy and a Model Predictive and Logic Control (MP-LC) framework are developed. These methods modulate the amplitude and frequency of braking torque reductions executed by the ABS to suppress high-frequency wheel oscillations, while ensuring sufficient braking force. Experimental validation using a real vehicle demonstrates that the proposed method increases the Mean Fully Developed Deceleration (MFDD) by 14.8% on low-adhesion surfaces and 15.2% on high-adhesion surfaces. Furthermore, the strategy significantly suppresses 12–13 Hz high-frequency oscillations, restoring normal ABS control cycles and enhancing both braking performance and ride comfort. Full article

(This article belongs to the Special Issue Advances in Dynamics and Control of Vehicles)

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19 pages, 3976 KiB

Open AccessArticle

Improving Centrifugal Pump Performance and Efficiency Using Composite Materials Through Additive Manufacturing

by Vasileios Papageorgiou, Gabriel Mansour and Ilias Chouridis

Machines 2025, 13(8), 729; https://doi.org/10.3390/machines13080729 - 17 Aug 2025

Viewed by 263

Abstract

Additive Manufacturing is a rapidly developing technology that enables the fabrication of objects with complex geometries and high levels of customization while keeping the prototyping costs relatively low. In recent years, its application has grown to include the fabrication of end-use parts, creating [...] Read more.

Additive Manufacturing is a rapidly developing technology that enables the fabrication of objects with complex geometries and high levels of customization while keeping the prototyping costs relatively low. In recent years, its application has grown to include the fabrication of end-use parts, creating new opportunities in industries such as the automotive, aerospace, mechanical, and hydraulic engineering industries. The present research paper focuses on the fabrication and evaluation of 3D-printed operational end-use parts of a water pump, which were originally made from cast iron. This approach aims to determine whether AM can be an alternative for metal parts in operational systems such as water pumps. In particular, the impeller of a centrifugal pump is remanufactured using material extrusion AM technology with PPS-CF composite polymer as a fabrication material. Subsequently, the surface roughness of the two parts is measured, and the performance of each part is predicted by creating a CFD model. Additionally, the printed part is compared to the original part by conducting a centrifugal pump performance test for each impeller. The results show that the 3D-printed impeller achieves an approximate 15% increase in overall efficiency compared to the original impeller. Full article

(This article belongs to the Section Turbomachinery)

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27 pages, 4201 KiB

Open AccessArticle

Design and Kinematic and Dynamic Analysis Simulation of a Biomimetic Parallel Mechanism for Lumbar Rehabilitation Exoskeleton

by Chao Hou, Zhicheng Yin, Di Wu, Rui Qian, Yu Tian and Hongbo Wang

Machines 2025, 13(8), 728; https://doi.org/10.3390/machines13080728 - 16 Aug 2025

Viewed by 179

Abstract

Lumbar disc herniation is one of the primary causes of lower back pain, and its incidence has significantly increased with the development of industrialization. To assist in rehabilitation therapy, this paper proposes a flexible exoskeleton for active lumbar rehabilitation based on a 4-SPU/SP [...] Read more.

Lumbar disc herniation is one of the primary causes of lower back pain, and its incidence has significantly increased with the development of industrialization. To assist in rehabilitation therapy, this paper proposes a flexible exoskeleton for active lumbar rehabilitation based on a 4-SPU/SP biomimetic parallel mechanism. By analyzing the anatomical structure and movement mechanisms of the lumbar spine, a four degree of freedom parallel mechanism was designed to mimic the three-axis rotation of the lumbar spine around the coronal, sagittal, and vertical axes, as well as movement along the z-axis. Using a 3D motion capture system, data on the range of motion of the lumbar spine was obtained to guide the structural design of the exoskeleton. Using the vector chain method, the display equations for the drive joints of the mechanism were derived, and forward and inverse kinematic models were established and simulated to verify their accuracy. The dynamic characteristics of the biomimetic parallel mechanism were analyzed and simulated to provide a theoretical basis for the design of the exoskeleton control system. A prototype was fabricated and tested to evaluate its maximum range of motion and workspace. Experimental results showed that after wearing the exoskeleton, the lumbar spine’s range of motion could still reach over 83.5% of the state without the exoskeleton, and its workspace could meet the lumbar spine movement requirements for daily life, verifying the rationality and feasibility of the proposed 4-SPU/SP biomimetic parallel mechanism design. Full article

(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)

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26 pages, 3407 KiB

Open AccessReview

Skyhook-Based Techniques for Vehicle Suspension Control: A Review of the State of the Art

by Jiyuan Wang, Zhenxing Huang, Haodong Hong, Siyao Yu, Weihan Shi and Xiaoliang Zhang

Machines 2025, 13(8), 727; https://doi.org/10.3390/machines13080727 - 15 Aug 2025

Viewed by 216

Abstract

Automotive suspension systems are key to improving ride comfort and handling stability. Over the past decades, active and semi-active suspensions have become a focal point in automotive engineering and have been widely adopted in the industry. Skyhook-based control and its related methodologies, as [...] Read more.

Automotive suspension systems are key to improving ride comfort and handling stability. Over the past decades, active and semi-active suspensions have become a focal point in automotive engineering and have been widely adopted in the industry. Skyhook-based control and its related methodologies, as a mature and viable solution, have been extensively implemented in vehicles. Despite the large number of research papers available on this topic, there remains a lack of comprehensive and up-to-date surveys in the literature that compare various Skyhook-based suspension control systems and their effectiveness. To bridge this gap, this paper systematically reviews the research progress in active and semi-active suspension controllers based on Skyhook principles over recent decades. Representative methods within major control rules are reported, and their characteristics, along with critical performance metrics, are critically analyzed. This paper also explores the development trends of Skyhook-based control. Full article

(This article belongs to the Special Issue Advances in Vehicle Suspension System Optimization and Control)

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21 pages, 3052 KiB

Open AccessArticle

Sensitivity Analysis of a Statistical Method for the Dynamic Coefficients Computations of a Tilting Pad Journal Bearing

by Michele Barsanti, Alberto Betti, Enrico Ciulli, Paola Forte and Matteo Nuti

Machines 2025, 13(8), 726; https://doi.org/10.3390/machines13080726 - 15 Aug 2025

Viewed by 143

Abstract

In this paper, an innovative method for the determination of the dynamic coefficients of tilting pad journal bearings (TPJBs) is described, and some of its characteristics are analyzed. The calculation is based on a parabolic modeling of the dependence of the dynamic coefficients [...] Read more.

In this paper, an innovative method for the determination of the dynamic coefficients of tilting pad journal bearings (TPJBs) is described, and some of its characteristics are analyzed. The calculation is based on a parabolic modeling of the dependence of the dynamic coefficients on the excitation frequency, on the estimation of the forces acting on the bearing as a function of the estimated displacements using a linear model and, finally, on the search for the best estimate of the parabola coefficients by minimizing the sum of the squares of the normalized residuals of displacements and forces on the bearings. The normalization is performed by dividing the deviations (between the measured values and those calculated by the model) by an estimate of the standard deviation of the force and displacement measurements. The results for a flooded tilting pad journal bearing, TPJB, are presented and compared with those obtained using traditional methods. The synchronous coefficients are also calculated and compared with those determined by linear interpolation. A preliminary statistical analysis of the sensitivity of the results to the variation in the standard deviation of the forces and displacements is presented. An extension of the model is proposed so that the coefficients of the optimal parabolas can be estimated as a function of the shaft rotation frequency. Full article

(This article belongs to the Special Issue Advances in Mechanism and Machine Science Within the IFIT 2024 Conference)

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25 pages, 3845 KiB

Open AccessArticle

Lagrangian Simulation of Sediment Erosion in Francis Turbines Using a Computational Tool in Python Coupled with OpenFOAM

by Mateo Narváez, Jeremy Guamán, Víctor Hugo Hidalgo, Modesto Pérez-Sánchez and Helena M. Ramos

Machines 2025, 13(8), 725; https://doi.org/10.3390/machines13080725 - 15 Aug 2025

Viewed by 181

Abstract

Hydraulic erosion from suspended sediment is a major degradation mechanism in Francis turbines of sediment-laden rivers, especially in Andean hydropower plants. This study presents a Python3.9-based computational tool integrating the empirical Oka erosion model within a Lagrangian particle tracking framework, coupled to single-phase [...] Read more.

Hydraulic erosion from suspended sediment is a major degradation mechanism in Francis turbines of sediment-laden rivers, especially in Andean hydropower plants. This study presents a Python3.9-based computational tool integrating the empirical Oka erosion model within a Lagrangian particle tracking framework, coupled to single-phase CFD in OpenFOAM 10. The novelty lies in a reduced-domain approach that omits the spiral casing and replicates its particle-induced swirl via a custom algorithm, lowering meshing complexity and computational cost while preserving erosion prediction accuracy. The method was applied to a full-scale Francis turbine at the San Francisco hydropower plant in Ecuador (nominal discharge 62.4 m³/s, rated output 115 MW, rotational speed 34.27 rad/s), operating under volcanic and erosive sediment loads. Maximum erosion rates reached ~1.2 × 10⁻⁴ mm³/kg, concentrated on runner blade trailing edges and guide vane pressure sides. Impact kinematics showed most collisions at near-normal angles (85°–98°, peak at 92°) and 6–9 m/s velocities, with rare 40 m/s impacts causing over 50× more loss than average. The workflow identifies critical wear zones, supports redesign and coating strategies, and offers a transferable, open-source framework for erosion assessment in turbines under diverse sediment-laden conditions. Full article

(This article belongs to the Special Issue Sustainable Manufacturing and Green Processing Methods, 2nd Edition)

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20 pages, 2424 KiB

Open AccessArticle

Predicting Vehicle-Engine-Radiated Noise Based on Bench Test and Machine Learning

by Ruijun Liu, Yingqi Yin, Yuming Peng and Xu Zheng

Machines 2025, 13(8), 724; https://doi.org/10.3390/machines13080724 - 15 Aug 2025

Viewed by 253

Abstract

As engines trend toward miniaturization, lightweight design, and higher power density, noise issues have become increasingly prominent, necessitating precise radiated noise prediction for effective noise control. This study develops a machine learning model based on surface vibration test data, which enhances the efficiency [...] Read more.

As engines trend toward miniaturization, lightweight design, and higher power density, noise issues have become increasingly prominent, necessitating precise radiated noise prediction for effective noise control. This study develops a machine learning model based on surface vibration test data, which enhances the efficiency of engine noise prediction and has the potential to serve as an alternative to traditional high-cost engine noise test methods. Experiments were conducted on a four-cylinder, four-stroke diesel engine, collecting surface vibration and radiated noise data under full-load conditions (1600–3000 r/min). Five prediction models were developed using support vector regression (SVR, including linear, polynomial, and radial basis function kernels), random forest regression, and multilayer perceptron, suitable for non-anechoic environments. The models were trained on time-domain and frequency-domain vibration data, with performance evaluated using the maximum absolute error, mean absolute error, and median absolute error. The results show that polynomial kernel SVR performs best in time domain modelling, with an average relative error of 0.10 and a prediction accuracy of up to 90%, which is 16% higher than that of MLP; the model does not require Fourier transform and principal component analysis, and the computational overhead is low, but it needs to collect data from multiple measurement points. The linear kernel SVR works best in frequency domain modelling, with an average relative error of 0.18 and a prediction accuracy of about 82%, which is suitable for single-point measurement scenarios with moderate accuracy requirements. Analysis of measurement points indicates optimal performance using data from the engine top between cylinders 3 and 4. This approach reduces reliance on costly anechoic facilities, providing practical value for noise control and design optimization. Full article

(This article belongs to the Special Issue Intelligent Applications in Mechanical Engineering)

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7 pages, 199 KiB

Open AccessEditorial

Advances in Noise and Vibrations for Machines

by Lukasz Scislo, Davide Astolfi and Francesco Castellani

Machines 2025, 13(8), 723; https://doi.org/10.3390/machines13080723 - 14 Aug 2025

Viewed by 254

Abstract

Vibration analysis and monitoring are currently required in various fields of industry, from automotive and aeronautics to manufacturing and quality control, and from machining and maintenance to civil engineering [...] Full article

(This article belongs to the Special Issue Advances in Noise and Vibrations for Machines)

22 pages, 7761 KiB

Open AccessArticle

Bearing-Weak-Fault Signal Enhancement and Diagnosis Based on Multivariate Statistical Hilbert Differential TEO

by Zhiqiang Liao, Renchao Cai, Zhijia Yan, Peng Chen and Xuewei Song

Machines 2025, 13(8), 722; https://doi.org/10.3390/machines13080722 - 13 Aug 2025

Viewed by 185

Abstract

The enhancement of weak-fault signal characteristics in rolling bearings under strong background noise interference has always been a challenging problem in rotating machinery fault diagnosis. Research indicates that multivariate statistical indicators such as skewness and kurtosis can characterize the fault features of vibration [...] Read more.

The enhancement of weak-fault signal characteristics in rolling bearings under strong background noise interference has always been a challenging problem in rotating machinery fault diagnosis. Research indicates that multivariate statistical indicators such as skewness and kurtosis can characterize the fault features of vibration signals. However, when the fault features in the signal are weak and severely affected by noise, the characterization capability of these indicators diminishes, significantly compromising diagnostic accuracy. To address this issue, this paper proposes a novel multivariate statistical filtering (MSF) method for multi-band filtering, which can effectively screen the target fault information bands in vibration signals during bearing faults. The core idea involves constructing a multivariate matrix of fused-fault multidimensional features by integrating fault and healthy signals, and then utilizing eigenvalue distance metrics to significantly characterize the spectral differences between fault and healthy signals. This enables the selection of frequency bands containing the most informative fault features from the segmented frequency spectrum. To address the inherent in-band residual noise in the MSF-processed signals, this paper further proposes the Hilbert differential Teager energy operator (HDTEO) based on MSF to suppress the filtered in-band noise, thereby enhancing transient fault impulses more effectively. The proposed method has been validated using both public datasets and laboratory datasets. Results demonstrate its effectiveness in accurately identifying fault characteristic frequencies, even under challenging conditions such as incipient bearing faults or severely weak vibration signatures caused by strong background noise. Finally, comparative experiments confirm the superior performance of the proposed approach. Full article

(This article belongs to the Section Machines Testing and Maintenance)

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26 pages, 16083 KiB

Open AccessArticle

Impact of the Magnetic Gap in Submerged Axial Flux Motors on Centrifugal Pump Hydraulic Performance and Internal Flow

by Qiyuan Zhu, Yandong Gu and Junjie Bian

Machines 2025, 13(8), 721; https://doi.org/10.3390/machines13080721 - 13 Aug 2025

Viewed by 247

Abstract

The integration of axial flux motors into canned motor pumps offers a promising approach to overcome the efficiency and size limitations of traditional designs, particularly in critical sectors like aerospace. However, the hydrodynamics in magnetic gap between the stator and rotor are poorly [...] Read more.

The integration of axial flux motors into canned motor pumps offers a promising approach to overcome the efficiency and size limitations of traditional designs, particularly in critical sectors like aerospace. However, the hydrodynamics in magnetic gap between the stator and rotor are poorly understood. This study investigates the effect of magnetic gap on performance and internal flow. Six magnetic gap schemes are developed, ranging from 0.2 to 1.2 mm. Numerical simulations are conducted, and simulation results showed good agreement with experimental data. The magnetic gap exhibits a non-linear effect on performance. The peak head coefficient occurs at a 0.4 mm gap and maximum efficiency at 1.0 mm. At a 0.2 mm gap, strong viscous shear forces increase disk friction loss and create high-vorticity flow. As the gap widens, flow transitions from viscosity-dominated to inertia-dominated, leading to a more ordered flow structure. The blade passing frequency is the dominant frequency. For a gap of 0.8 mm, the pressure fluctuation intensity is lowest. By analyzing performance, head coefficient, velocity, vorticity, entropy production, and pressure fluctuations, a gap of 0.8 mm is identified as the optimal design. This study provides critical guidance for optimizing the design of axial flux canned motor pumps. Full article

(This article belongs to the Special Issue Advanced Research and Development in Fluid Machinery: Design, Optimization, and Applications)

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13 pages, 3944 KiB

Open AccessArticle

Design and Analysis of a Double-Three-Phase Permanent Magnet Fault-Tolerant Machine with Low Short-Circuit Current for Flywheel Energy Storage

by Xiaotong Li, Shaowei Liang, Buyang Qi, Zhenghui Zhao and Zhijian Ling

Machines 2025, 13(8), 720; https://doi.org/10.3390/machines13080720 - 13 Aug 2025

Viewed by 263

Abstract

This paper proposes a double-three-phase permanent magnet fault-tolerant machine (DTP-PMFTM) with low short-circuit current for flywheel energy storage systems (FESS) to balance torque performance and short-circuit current suppression. The key innovation lies in its modular winding configuration that ensures electrical isolation between the [...] Read more.

This paper proposes a double-three-phase permanent magnet fault-tolerant machine (DTP-PMFTM) with low short-circuit current for flywheel energy storage systems (FESS) to balance torque performance and short-circuit current suppression. The key innovation lies in its modular winding configuration that ensures electrical isolation between the two winding sets. First, the structural characteristics of the double three-phase windings are analyzed. Subsequently, the harmonic features of the resultant magnetomotive force (MMF) are systematically investigated. To verify the performance, the proposed machine is compared against a conventional winding structure as a baseline, focusing on key parameters such as output torque and short-circuit current. The experimental results demonstrate that the proposed machine achieves an average torque of approximately 14.7 N·m with a torque ripple of about 3.27%, a phase inductance of approximately 3.7 mH, and a short-circuit current of approximately 50.9 A. Crucially, compared to the conventional winding, the modular structure increases the phase inductance by about 32.1% and reduces the short-circuit current by 29.7%. Finally, an experimental platform is established to validate the performance of the machine. Full article

(This article belongs to the Special Issue High-Power-Density and High-Reliability Permanent Magnet Synchronous Machine Systems: Design, Control, Applications, and Sustainability)

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Machines, Volume 13, Issue 8 (August 2025) – 118 articles

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