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Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring: Volume 2

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 75886

Special Issue Editors

Prof. Dr. Rafal Burdzik

E-Mail Website
Leading Guest Editor
Department of Road Transport, Faculty of Transport and Aviation Engineering, Silesian University of Technology, 40-019 Katowice, Poland
Interests: structural health monitoring; structural damage identification; structural dynamics; vibro-acoustics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Minvydas Ragulskis

E-Mail Website
Guest Editor
Department of Mathematical Modelling, Kaunas University of Technology, LT-51368 Kaunas, Lithuania
Interests: development of numerical methods; modelling of optical effects; visual cryptography; nonlinear dynamical systems and chaos
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Maosen Cao

E-Mail Website
Guest Editor
Department of Engineering Mechanics, College of Mechanics and Materials, Hohai University, Nanjing 210098, China
Interests: structural health monitoring; structural damage identification; smart materials and structures; applied soft computing; structural vibration and control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Radosław Zimroz

E-Mail Website
Guest Editor
Faculty of GeoEngineering Mining and Geology, Wrocław University of Science and Technology, Wrocław, Poland
Interests: mining machines; field measurements; condition monitoring; advanced signal processing; data analytics; acoustics; predictive maintenance
Special Issues, Collections and Topics in MDPI journals
Dr. Chaari Fakher

E-Mail Website
Guest Editor
National School of Engineers of Sfax, Sfax, Tunisia
Interests: machine and structure dynamics; vibro-acoustic behavior of machines and structures
Special Issues, Collections and Topics in MDPI journals
Dr. Łukasz Konieczny

E-Mail Website
Guest Editor
Silesian University of Technology, Katowice, Poland
Interests: structural analysis; finite element modeling; structural dynamics; automobile engineering; nonlinear analysis; dynamic analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

I would like to interest you in this Special Issue on “Sensing Technology and Data Interpretation in Machine Diagnosis and Systems, Part 2” at Sensors and cordially invite you to submit your articles. The purpose of this second edition Special Issue is to compile studies on knowledge, research practice, and forecast development trends in the field of machine and system diagnostics, with particular emphasis on measuring systems and signal processing methods to extract useful information. The dynamic development of the Smart concept in all engineering areas indicates the need for the consolidation and exchange of knowledge in this area, for which this Special Issue is an ideal platform. Taking this into account, it was decided to launch a second edition of this Special Issue in order to showcase the development and new applications.

Machine diagnosis and systems condition monitoring are fundamental processes for decision making protocols in all mechanical systems. Control and steering of all systems determine their operational and functional activities. These decisions must be made based on proper data. Therefore, the most appropriate data must first be obtained, and then the important information components must be separated. All components of the logical decision path that may degrade the quality of the data acquired or reduce operational reliability must be avoided and eliminated. Therefore, it is important to correctly indicate the data acquisition points, select the most suitable sensors, and correctly complete the entire measurement path and dedicated signal analysis.

This Special Issue will focus on recent attempts in the development of sensors and sensing technology due to novel possibilities in machine diagnosis and systems condition monitoring to underline this new knowledge and application, especially for trends in smart machines and systems with self-diagnosis properties. An example of this is the concept of smart cities and intelligent systems, which is trending worldwide. This Special Issue will collect interdisciplinary approaches on sensors and sensing technology in machine diagnosis and systems condition monitoring, including the consideration and development of some innovative directions in research.

The potential scope includes but is not limited to the following:

  • Methods and apparatuses for machine diagnosis and systems condition monitoring;
  • Signal processing, data fusion, and deep learning in sensor systems;
  • Damage detection and identification in machines;
  • Condition monitoring in systems;
  • Sensors in control and steering of the system;
  • 5G/6G technologies;
  • Identification of machinery non-stationary and anomalous operation;
  • Advanced signal processing methods for machine diagnosis and condition monitoring;
  • Practical cases of machine diagnosis and systems condition monitoring;
  • Machine and system assessment under noisy conditions;
  • Intelligent transport systems;
  • Sensor network and relationships;
  • Smart/intelligent sensors;
  • Sensor technology and application for machine diagnosis and systems condition monitoring;
  • Internet of Things for machine diagnosis and systems condition monitoring;
  • Localization and object tracking in smart cities;
  • Machine learning applications;
  • Complex machine and system analysis using multiple sensors;
  • Techniques for online, real-time system condition monitoring.

Prof. Dr. Rafal Burdzik
Prof. Dr. Minvydas Ragulskis
Prof. Dr. Maosen Cao
Prof. Dr. Radosław Zimroz
Dr. Chaari Fakher
Dr. Łukasz Konieczny
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine diagnosis
  • systems condition monitoring
  • sensors
  • sensing technology
  • smart city
  • IoT

Published Papers (28 papers)

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14 pages, 9985 KiB  
Article
Analysis of Changes in the Opening Pressure of Marine Engine Injectors Based on Vibration Parameters Recorded at a Constant Torque Load
by Marcin Kluczyk, Andrzej Grządziela, Adam Polak, Michał Pająk and Miłosz Gajda
Sensors 2023, 23(20), 8404; https://doi.org/10.3390/s23208404 - 12 Oct 2023
Cited by 1 | Viewed by 831
Abstract
This article deals with the problems related to the difficulties in the vibration diagnostics of modern marine engines. The focus was on the injection system, with a particular emphasis on injectors. An unusual approach to the implementation of research enabling the smooth regulation [...] Read more.
This article deals with the problems related to the difficulties in the vibration diagnostics of modern marine engines. The focus was on the injection system, with a particular emphasis on injectors. An unusual approach to the implementation of research enabling the smooth regulation of the opening pressure of the mechanical injector during engine operation at a constant load was presented. This approach obtained repeatability of conditions for subsequent measurements, which is very difficult to achieve when using the classic approach that forces the injector to be disassembled after each test. Full article
(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring: Volume 2)
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17 pages, 10815 KiB  
Article
Impact and Assessment of Suspension Stiffness on Vibration Propagation into Vehicle
by Rafał Burdzik
Sensors 2023, 23(4), 1930; https://doi.org/10.3390/s23041930 - 09 Feb 2023
Cited by 4 | Viewed by 1860
Abstract
The impact of transport-induced vibrations on people is a particularly important problem. Sudden or intensifying vibration phenomena of a local nature may compromise safety, especially in transport. The paper addresses the results of research on the impact of spring stiffness parameters on the [...] Read more.
The impact of transport-induced vibrations on people is a particularly important problem. Sudden or intensifying vibration phenomena of a local nature may compromise safety, especially in transport. The paper addresses the results of research on the impact of spring stiffness parameters on the propagation of vibrations in the vehicle structure using simple amplitude and frequency measures. The use of the developed method of selective multi-criteria analysis of frequency bands made it possible to compare the vibrations recorded in the vehicle with a new or worn coil spring. The results of the present study allow the development of a large data base in which all signals are classified by the exploitation parameters and location of the propagation of vibration in the vehicle. The most important findings and achievements of the presented study are the testing of actual suspension components with real damage under controlled conditions, the identification of the vibration propagation path from the wheel to the driver and passenger feet, the quantitative comparison of vibrations affecting humans in the vehicle (through the feet), and the frequency decomposition of vibration for selected bands. These findings improve the proper interpretation of the developed measures and, as a result, the difficulties in using this knowledge at the engineering level, for example, in the design and construction improvement stage. Therefore, innovation points and engineering significances are a method of selective multi-criteria analysis of frequency bands and have potential applications in diagnostics and the design of suspension systems and in terms of passengers’ comfort. Full article
(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring: Volume 2)
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21 pages, 7431 KiB  
Article
From Strain to Loads: Development of a Measurement Solution for Wind Turbine Transmission Input Loads during Drivetrain Testing
by Eren Bilen, Baher Azzam, Ralf Schelenz, Tim Runkel, Malte Raddatz and Georg Jacobs
Sensors 2023, 23(4), 1824; https://doi.org/10.3390/s23041824 - 06 Feb 2023
Viewed by 1494
Abstract
As wind energy is paving the way for the energy transition from fossil to renewable energy sources, the ongoing trend of increasing the rated power of wind turbines aims to reduce the overall cost of wind energy. The resulting increase in drivetrain loads [...] Read more.
As wind energy is paving the way for the energy transition from fossil to renewable energy sources, the ongoing trend of increasing the rated power of wind turbines aims to reduce the overall cost of wind energy. The resulting increase in drivetrain loads motivates the need for wind turbine (WT) drivetrain testing in the development phase of critical components such as the WT main gearbox (GB). While several WT system test benches allow for the application of emulated rotor loads in six degrees of freedom (6-DOF), the drivetrain input loads can significantly differ from the GB 6-DOF input loads due to the design of the drivetrain under test. However, currently available load measurement solutions are not capable of sensing GB input loads in 6-DOF. Thus, this work aims to develop a methodology for converging signals from a purposely designed sensor setup and turbine specific design parameters to compute the GB 6-DOF input loads during WT testing. Strain gauges (SG) and accelerometers have been installed on the low-speed shaft (LSS) of a WT drivetrain under test at the 4MW WT system test bench at the Center for Wind Power Drives. Using the data of the aforementioned sensors, a methodology for computing the GB input loads is developed. The methodology is validated through comparison to the applied loads data provided by the aforementioned test bench. The results demonstrate the high promise of the proposed method for estimating the GB input loads during WT drivetrain testing. Full article
(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring: Volume 2)
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26 pages, 19261 KiB  
Article
Demagnetization Fault Diagnosis of Permanent Magnet Synchronous Motors Based on Stator Current Signal Processing and Machine Learning Algorithms
by Przemyslaw Pietrzak and Marcin Wolkiewicz
Sensors 2023, 23(4), 1757; https://doi.org/10.3390/s23041757 - 04 Feb 2023
Cited by 12 | Viewed by 2923
Abstract
Reliable fault diagnosis and condition monitoring are essential for permanent magnet synchronous motor (PMSM) drive systems with high-reliability requirements. PMSMs can be subject to various types of damage during operation. Magnetic damage is a unique fault of PMSM and concerns the permanent magnet [...] Read more.
Reliable fault diagnosis and condition monitoring are essential for permanent magnet synchronous motor (PMSM) drive systems with high-reliability requirements. PMSMs can be subject to various types of damage during operation. Magnetic damage is a unique fault of PMSM and concerns the permanent magnet (PM) of the rotor. PM damage may be mechanical in nature or be related to the phenomenon of demagnetization. This article presents a machine learning (ML) based demagnetization fault diagnosis method for PMSM drives. The time-frequency domain analysis based on short-time Fourier transform (STFT) is applied in the process of PM fault feature extraction from the stator phase current signal. Moreover, two ML-based models are verified and compared in the process of the automatic fault detection of demagnetization fault. These models are k-nearest neighbors (KNN) and multiLayer perceptron (MLP). The influence of the input vector elements, key parameters and structures of the models used on their effectiveness is extensively analyzed. The results of the experimental verification confirm the very high effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring: Volume 2)
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14 pages, 9186 KiB  
Article
Tool-Wear-Estimation System in Milling Using Multi-View CNN Based on Reflected Infrared Images
by Woong-Ki Jang, Dong-Wook Kim, Young-Ho Seo and Byeong-Hee Kim
Sensors 2023, 23(3), 1208; https://doi.org/10.3390/s23031208 - 20 Jan 2023
Cited by 1 | Viewed by 1775
Abstract
A novel method for tool wear estimation in milling using infrared (IR) laser vision and a deep-learning algorithm is proposed and demonstrated. The measurement device employs an IR line laser to irradiate the tool focal point at angles of −7.5°, 0.0°, and +7.5° [...] Read more.
A novel method for tool wear estimation in milling using infrared (IR) laser vision and a deep-learning algorithm is proposed and demonstrated. The measurement device employs an IR line laser to irradiate the tool focal point at angles of −7.5°, 0.0°, and +7.5° to the vertical plane, and three cameras are placed at 45° intervals around the tool to collect the reflected IR light at different locations. For the processing materials and methods, a dry processing method was applied to a 100 mm × 100 mm × 40 mm SDK-11 workpiece through end milling and downward cutting using a TH308 insert. This device uses the diffused light reflected off the surface of a rotating tool roughened by flank wear, and a polarization filter is considered. As the measured tool wear images exhibit a low dynamic range of exposure, high dynamic range (HDR) images are obtained using an exposure fusion method. Finally, tool wear is estimated from the images using a multi-view convolutional neural network. As shown in the results of the estimated tool wear, a mean absolute error (MAE) of prediction error calculated was to be 9.5~35.21 μm. The proposed method can improve machining efficiency by reducing the downtime for tool wear measurement and by increasing tool life utilization. Full article
(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring: Volume 2)
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18 pages, 4868 KiB  
Article
Advanced Feature Extraction Methods from Images of Drillings in Melamine Faced Chipboard for Automatic Diagnosis of Drill Wear
by Izabella Antoniuk, Jarosław Kurek, Artur Krupa, Grzegorz Wieczorek, Michał Bukowski, Michał Kruk and Albina Jegorowa
Sensors 2023, 23(3), 1109; https://doi.org/10.3390/s23031109 - 18 Jan 2023
Cited by 1 | Viewed by 1847
Abstract
In this paper, a novel approach to evaluation of feature extraction methodologies is presented. In the case of machine learning algorithms, extracting and using the most efficient features is one of the key problems that can significantly influence overall performance. It is especially [...] Read more.
In this paper, a novel approach to evaluation of feature extraction methodologies is presented. In the case of machine learning algorithms, extracting and using the most efficient features is one of the key problems that can significantly influence overall performance. It is especially the case with parameter-heavy problems, such as tool condition monitoring. In the presented case, images of drilled holes are considered, where state of the edge and the overall size of imperfections have high influence on product quality. Finding and using a set of features that accurately describes the differences between the edge that is acceptable or too damaged is not always straightforward. The presented approach focuses on detailed evaluation of various feature extraction approaches. Each chosen method produced a set of features, which was then used to train a selected set of classifiers. Five initial feature sets were obtained, and additional ones were derived from them. Different voting methods were used for ensemble approaches. In total, 38 versions of the classifiers were created and evaluated. Best accuracy was obtained by the ensemble approach based on Weighted Voting methodology. A significant difference was shown between different feature extraction methods, with a total difference of 11.14% between the worst and best feature set, as well as a further 0.2% improvement achieved by using the best voting approach. Full article
(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring: Volume 2)
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17 pages, 7649 KiB  
Article
A Virtual Combustion Sensor Based on Ion Current for Lean-Burn Natural Gas Engine
by Xiaoyan Wang, Tanqing Zhou, Quan Dong, Zhaolin Cheng and Xiyu Yang
Sensors 2022, 22(13), 4660; https://doi.org/10.3390/s22134660 - 21 Jun 2022
Cited by 3 | Viewed by 2067
Abstract
In this study, an innovative sensor was designed to detect the key combustion parameters of the marine natural gas engine. Based on the ion current, any engine structurally modified was avoided and the real-time monitoring for the combustion process was realized. For the [...] Read more.
In this study, an innovative sensor was designed to detect the key combustion parameters of the marine natural gas engine. Based on the ion current, any engine structurally modified was avoided and the real-time monitoring for the combustion process was realized. For the general applicability of the proposed sensor, the ion current generated by a high-energy ignition system was acquired in a wide operating range of the engine. It was found that engine load, excess air coefficient (λ) and ignition timing all generated great influence on both the chemical and thermal phases, which indicated that the ion current was highly correlated with the combustion process in the cylinder. Furthermore, the correlations between the 5 ion current-related parameters and the 10 combustion-related parameters were analyzed in detail. The results showed that most correlation coefficients were relatively high. Based on the aforementioned high correlation, the novel sensor used an on-line algorithm at the basis of neural network models. The models took the characteristic values extracted from the ion current as the inputs and the key combustion parameters as the outputs to realize the online combustion sensing. Four neural network models were established according to the existence of the thermal phase peak of the ion current and two different network structures (BP and RBF). Finally, the predicted values of the four models were compared with the experimental values. The results showed that the BP (with thermal) model had the highest prediction accuracy of phase parameters and amplitude parameters of combustion. Meanwhile, RBF (with thermal) model had the highest prediction accuracy of emission parameters. The mean absolute percentage errors (MAPE) were mostly lower than 0.25, which proved a high accuracy of the proposed ion current-based virtual sensor for detecting the key combustion parameters. Full article
(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring: Volume 2)
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17 pages, 2461 KiB  
Article
Assessing the Suitability of DME for Powering SI Engines by Analyzing In-Cylinder Pressure Change
by Paweł Fabiś
Sensors 2022, 22(12), 4505; https://doi.org/10.3390/s22124505 - 14 Jun 2022
Cited by 2 | Viewed by 1389
Abstract
This article discusses an analysis of in-cylinder pressure change during combustion of LPG-DME fuel in IC engines. The aim of the study is to present a method for assessing the possibility of using DME as a combustion activator, and to establish its impact [...] Read more.
This article discusses an analysis of in-cylinder pressure change during combustion of LPG-DME fuel in IC engines. The aim of the study is to present a method for assessing the possibility of using DME as a combustion activator, and to establish its impact on the process. The study proposes a method for assessing the shift of the maximum value of cylinder pressure as a parameter which enables the impact of DME on the combustion process to be evaluated. The method was developed on the basis of bench tests carried out on an SI engine with a capacity of 1.6 dm3. Full article
(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring: Volume 2)
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22 pages, 23903 KiB  
Article
A Complex Vibration Analysis of a Drive System Equipped with an Innovative Prototype of a Flexible Torsion Clutch as an Element of Pre-Implementation Testing
by Andrzej N. Wieczorek, Łukasz Konieczny, Rafał Burdzik, Grzegorz Wojnar, Krzysztof Filipowicz and Mariusz Kuczaj
Sensors 2022, 22(6), 2183; https://doi.org/10.3390/s22062183 - 11 Mar 2022
Cited by 5 | Viewed by 2718
Abstract
The paper presents how an important aspect of introducing new machines, especially in the mining industry, is testing a prototype under laboratory conditions. For this purpose, advanced methods of analyzing the vibrations of a drive system equipped with an innovative prototype of a [...] Read more.
The paper presents how an important aspect of introducing new machines, especially in the mining industry, is testing a prototype under laboratory conditions. For this purpose, advanced methods of analyzing the vibrations of a drive system equipped with an innovative prototype of a flexible torsion clutch are presented. The main goal is to present a comprehensive method for analyzing vibration signals in various dimensions of the signal analysis. As a result of this approach, it can be seen how much important information about the tested clutch can be obtained by using various analysis methods in terms of time–frequency distributions or order analysis. To emphasize the differences in the functioning of the tested clutch and the possibility of monitoring these differences on the basis of the observation of residual processes, such as vibrations, the results for the flexible and locked clutch are compared. Full article
(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring: Volume 2)
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15 pages, 1123 KiB  
Article
A New Statistical Features Based Approach for Bearing Fault Diagnosis Using Vibration Signals
by Muhammad Altaf, Tallha Akram, Muhammad Attique Khan, Muhammad Iqbal, M Munawwar Iqbal Ch and Ching-Hsien Hsu
Sensors 2022, 22(5), 2012; https://doi.org/10.3390/s22052012 - 04 Mar 2022
Cited by 53 | Viewed by 5436
Abstract
In condition based maintenance, different signal processing techniques are used to sense the faults through the vibration and acoustic emission signals, received from the machinery. These signal processing approaches mostly utilise time, frequency, and time-frequency domain analysis. The features obtained are later integrated [...] Read more.
In condition based maintenance, different signal processing techniques are used to sense the faults through the vibration and acoustic emission signals, received from the machinery. These signal processing approaches mostly utilise time, frequency, and time-frequency domain analysis. The features obtained are later integrated with the different machine learning techniques to classify the faults into different categories. In this work, different statistical features of vibration signals in time and frequency domains are studied for the detection and localisation of faults in the roller bearings. These are later classified into healthy, outer race fault, inner race fault, and ball fault classes. The statistical features including skewness, kurtosis, average and root mean square values of time domain vibration signals are considered. These features are extracted from the second derivative of the time domain vibration signals and power spectral density of vibration signals. The vibration signal is also converted to the frequency domain and the same features are extracted. All three feature sets are concatenated, creating the time, frequency and spectral power domain feature vectors. These feature vectors are finally fed into the K- nearest neighbour, support vector machine and kernel linear discriminant analysis for the detection and classification of bearing faults. With the proposed method, the reduction percentage of more than 95% percent is achieved, which not only reduces the computational burden but also the classification time. Simulation results show that the signals are classified to achieve an average accuracy of 99.13% using KLDA and 96.64% using KNN classifiers. The results are also compared with the empirical mode decomposition (EMD) features and Fourier transform features without extracting any statistical information, which are two of the most widely used approaches in the literature. To gain a certain level of confidence in the classification results, a detailed statistical analysis is also provided. Full article
(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring: Volume 2)
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16 pages, 4750 KiB  
Article
Correlation Coefficient Based Optimal Vibration Sensor Placement and Number
by Geon-Ho Shin and Jang-Wook Hur
Sensors 2022, 22(3), 1207; https://doi.org/10.3390/s22031207 - 05 Feb 2022
Cited by 5 | Viewed by 1988
Abstract
Vibration sensors are mostly used for fault diagnoses of machines or structures. If more sensors are applied, more accurate fault diagnosis is possible. However, it will obviously cost more. There are many approaches to optimize the number and installation location/point of vibration sensors [...] Read more.
Vibration sensors are mostly used for fault diagnoses of machines or structures. If more sensors are applied, more accurate fault diagnosis is possible. However, it will obviously cost more. There are many approaches to optimize the number and installation location/point of vibration sensors for more efficient fault diagnosis. Existing methods require a great deal of computational throughput for optimization when considering many mode frequencies with points where vibration sensors are likely to be installed. This paper proposes a practical way of optimizing the sensor installation point considering many mode frequencies with lots of places for sensor installation. FEA was conducted to identify displacement values of each frequency in the candidate points. Then, correlation coefficients were applied to the FEA result to optimize the installation location and number of vibration sensors. Taking into account cases where the number of sensors has been set by users, FIM was applied. The correlation coefficient optimized the candidate points where 24,252 vibration sensors were to be installed and reduced this to 10 points. FIM, which was not suitable for directly optimizing sensor locations because it required a lot of computational throughput and was usually applied to evaluate other methods, is now applicable to candidate points that have been reduced by the correlation coefficient. This paper does not draw the best optimal sensor location but presents a way to apply to large-scale or complicated forms with a little computational throughput. Full article
(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring: Volume 2)
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25 pages, 6994 KiB  
Article
Seismic Damage Identification Method for Curved Beam Bridges Based on Wavelet Packet Norm Entropy
by Tongfa Deng, Jinwen Huang, Maosen Cao, Dayang Li and Mahmoud Bayat
Sensors 2022, 22(1), 239; https://doi.org/10.3390/s22010239 - 29 Dec 2021
Cited by 5 | Viewed by 2016
Abstract
Curved beam bridges, whose line type is flexible and beautiful, are an indispensable bridge type in modern traffic engineering. Nevertheless, compared with linear bridges, curved beam bridges have more complex internal forces and deformation due to the curvature; therefore, this type of bridge [...] Read more.
Curved beam bridges, whose line type is flexible and beautiful, are an indispensable bridge type in modern traffic engineering. Nevertheless, compared with linear bridges, curved beam bridges have more complex internal forces and deformation due to the curvature; therefore, this type of bridge is more likely to suffer damage in strong earthquakes. The occurrence of damage reduces the safety of bridges, and can even cause casualties and property loss. For this reason, it is of great significance to study the identification of seismic damage in curved beam bridges. However, there is currently little research on curved beam bridges. For this reason, this paper proposes a damage identification method based on wavelet packet norm entropy (WPNE) under seismic excitation. In this method, wavelet packet transform is adopted to highlight the damage singularity information, the Lp norm entropy of wavelet coefficient is taken as a damage characteristic factor, and then the occurrence of damage is characterized by changes in the damage index. To verify the feasibility and effectiveness of this method, a finite element model of Curved Continuous Rigid-Frame Bridges (CCRFB) is established for the purposes of numerical simulation. The results show that the damage index based on WPNE can accurately identify the damage location and characterize the severity of damage; moreover, WPNE is more capable of performing damage location and providing early warning than the method based on wavelet packet energy. In addition, noise resistance analysis shows that WPNE is immune to noise interference to a certain extent. As long as a series of frequency bands with larger correlation coefficients are selected for WPNE calculation, independent noise reduction can be achieved. Full article
(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring: Volume 2)
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19 pages, 1613 KiB  
Article
A High-Dimensional and Small-Sample Submersible Fault Detection Method Based on Feature Selection and Data Augmentation
by Penghui Zhao, Qinghe Zheng, Zhongjun Ding, Yi Zhang, Hongjun Wang and Yang Yang
Sensors 2022, 22(1), 204; https://doi.org/10.3390/s22010204 - 29 Dec 2021
Cited by 4 | Viewed by 2018
Abstract
The fault detection of manned submersibles plays a very important role in protecting the safety of submersible equipment and personnel. However, the diving sensor data is scarce and high-dimensional, so this paper proposes a submersible fault detection method, which is made up of [...] Read more.
The fault detection of manned submersibles plays a very important role in protecting the safety of submersible equipment and personnel. However, the diving sensor data is scarce and high-dimensional, so this paper proposes a submersible fault detection method, which is made up of feature selection module based on hierarchical clustering and Autoencoder (AE), the improved Deep Convolutional Generative Adversarial Networks (DCGAN)-based data augmentation module and fault detection module using Convolutional Neural Network (CNN) with LeNet-5 structure. First, feature selection is developed to select the features that have a strong correlation with failure event. Second, data augmentation model is conducted to generate sufficient data for training the CNN model, including rough data generation and data refiners. Finally, a fault detection framework with LeNet-5 is trained and fine-tuned by synthetic data, and tested using real data. Experiment results based on sensor data from submersible hydraulic system demonstrate that our proposed method can successfully detect the fault samples. The detection accuracy of proposed method can reach 97% and our method significantly outperforms other classic detection algorithms. Full article
(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring: Volume 2)
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22 pages, 8324 KiB  
Article
Comparative Analysis of Machine Learning Methods for Predicting Robotized Incremental Metal Sheet Forming Force
by Vytautas Ostasevicius, Ieva Paleviciute, Agne Paulauskaite-Taraseviciene, Vytautas Jurenas, Darius Eidukynas and Laura Kizauskiene
Sensors 2022, 22(1), 18; https://doi.org/10.3390/s22010018 - 21 Dec 2021
Cited by 8 | Viewed by 2896
Abstract
This paper proposes a method for extracting information from the parameters of a single point incremental forming (SPIF) process. The measurement of the forming force using this technology helps to avoid failures, identify optimal processes, and to implement routine control. Since forming forces [...] Read more.
This paper proposes a method for extracting information from the parameters of a single point incremental forming (SPIF) process. The measurement of the forming force using this technology helps to avoid failures, identify optimal processes, and to implement routine control. Since forming forces are also dependent on the friction between the tool and the sheet metal, an innovative solution has been proposed to actively control the friction forces by modulating the vibrations that replace the environmentally unfriendly lubrication of contact surfaces. This study focuses on the influence of mechanical properties, process parameters and sheet thickness on the maximum forming force. Artificial Neural Network (ANN) and different machine learning (ML) algorithms have been applied to develop an efficient force prediction model. The predicted forces agreed reasonably well with the experimental results. Assuming that the variability of each input function is characterized by a normal distribution, sampling data were generated. The applicability of the models in an industrial environment is due to their relatively high performance and the ability to balance model bias and variance. The results indicate that ANN and Gaussian process regression (GPR) have been identified as the most efficient methods for developing forming force prediction models. Full article
(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring: Volume 2)
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27 pages, 3549 KiB  
Article
Divergence-Based Segmentation Algorithm for Heavy-Tailed Acoustic Signals with Time-Varying Characteristics
by Aleksandra Grzesiek, Karolina Gąsior, Agnieszka Wyłomańska and Radosław Zimroz
Sensors 2021, 21(24), 8487; https://doi.org/10.3390/s21248487 - 20 Dec 2021
Cited by 4 | Viewed by 2053
Abstract
Many real-world systems change their parameters during the operation. Thus, before the analysis of the data, there is a need to divide the raw signal into parts that can be considered as homogeneous segments. In this paper, we propose a segmentation procedure that [...] Read more.
Many real-world systems change their parameters during the operation. Thus, before the analysis of the data, there is a need to divide the raw signal into parts that can be considered as homogeneous segments. In this paper, we propose a segmentation procedure that can be applied for the signal with time-varying characteristics. Moreover, we assume that the examined signal exhibits impulsive behavior, thus it corresponds to the so-called heavy-tailed class of distributions. Due to the specific behavior of the data, classical algorithms known from the literature cannot be used directly in the segmentation procedure. In the considered case, the transition between parts corresponding to homogeneous segments is smooth and non-linear. This causes that the segmentation algorithm is more complex than in the classical case. We propose to apply the divergence measures that are based on the distance between the probability density functions for the two examined distributions. The novel segmentation algorithm is applied to real acoustic signals acquired during coffee grinding. Justification of the methodology has been performed experimentally and using Monte-Carlo simulations for data from the model with heavy-tailed distribution (here the stable distribution) with time-varying parameters. Although the methodology is demonstrated for a specific case, it can be extended to any process with time-changing characteristics. Full article
(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring: Volume 2)
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22 pages, 8260 KiB  
Article
System for Tool-Wear Condition Monitoring in CNC Machines under Variations of Cutting Parameter Based on Fusion Stray Flux-Current Processing
by Arturo Yosimar Jaen-Cuellar, Roque Alfredo Osornio-Ríos, Miguel Trejo-Hernández, Israel Zamudio-Ramírez, Geovanni Díaz-Saldaña, José Pablo Pacheco-Guerrero and Jose Alfonso Antonino-Daviu
Sensors 2021, 21(24), 8431; https://doi.org/10.3390/s21248431 - 17 Dec 2021
Cited by 15 | Viewed by 3490
Abstract
The computer numerical control (CNC) machine has recently taken a fundamental role in the manufacturing industry, which is essential for the economic development of many countries. Current high quality production standards, along with the requirement for maximum economic benefits, demand the use of [...] Read more.
The computer numerical control (CNC) machine has recently taken a fundamental role in the manufacturing industry, which is essential for the economic development of many countries. Current high quality production standards, along with the requirement for maximum economic benefits, demand the use of tool condition monitoring (TCM) systems able to monitor and diagnose cutting tool wear. Current TCM methodologies mainly rely on vibration signals, cutting force signals, and acoustic emission (AE) signals, which have the common drawback of requiring the installation of sensors near the working area, a factor that limits their application in practical terms. Moreover, as machining processes require the optimal tuning of cutting parameters, novel methodologies must be able to perform the diagnosis under a variety of cutting parameters. This paper proposes a novel non-invasive method capable of automatically diagnosing cutting tool wear in CNC machines under the variation of cutting speed and feed rate cutting parameters. The proposal relies on the sensor information fusion of spindle-motor stray flux and current signals by means of statistical and non-statistical time-domain parameters, which are then reduced by means of a linear discriminant analysis (LDA); a feed-forward neural network is then used to automatically classify the level of wear on the cutting tool. The proposal is validated with a Fanuc Oi mate Computer Numeric Control (CNC) turning machine for three different cutting tool wear levels and different cutting speed and feed rate values. Full article
(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring: Volume 2)
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21 pages, 2589 KiB  
Article
Online Frequency Response Analysis of Electric Machinery through an Active Coupling System Based on Power Electronics
by Wilson Cesar Sant’Ana, Germano Lambert-Torres, Erik Leandro Bonaldi, Bruno Reno Gama, Tiago Goncalves Zacarias, Isac Antonio dos Santos Areias, Daniel de Almeida Arantes, Frederico de Oliveira Assuncao, Mateus Mendes Campos and Fabio Monteiro Steiner
Sensors 2021, 21(23), 8057; https://doi.org/10.3390/s21238057 - 02 Dec 2021
Cited by 8 | Viewed by 2003
Abstract
This paper presents an innovative concept for the online application of Frequency Response Analysis (FRA). FRA is a well known technique that is applied to detect damage in electric machinery. As an offline technique, the machine under testing has to be removed from [...] Read more.
This paper presents an innovative concept for the online application of Frequency Response Analysis (FRA). FRA is a well known technique that is applied to detect damage in electric machinery. As an offline technique, the machine under testing has to be removed from service—which may cause loss of production. Experimental adaptations of FRA to online operation are usually based on the use of passive high pass coupling—which, ideally, should provide attenuation to the grid voltage, and at the same time, allow the high frequency FRA signals to be injected at the machine. In practice, however, the passive coupling results in a trade-off between the required attenuation and the useful area obtained at the FRA spectra. This paper proposes the use of an active coupling system, based on power electronics, in order to cancel the grid voltage at the terminals of FRA equipment and allow its safe connection to an energized machine. The paper presents the basic concepts of FRA and the issue of online measurements. It also presents basic concepts about power electronics converters and the operating principles of the Modular Multilevel Converter, which enables the generation of an output voltage with low THD, which is important for tracking the grid voltage with minimum error. Full article
(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring: Volume 2)
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22 pages, 4272 KiB  
Article
Vibration Monitoring of Civil Engineering Structures Using Contactless Vision-Based Low-Cost IATS Prototype
by Rinaldo Paar, Ante Marendić, Ivan Jakopec and Igor Grgac
Sensors 2021, 21(23), 7952; https://doi.org/10.3390/s21237952 - 28 Nov 2021
Cited by 9 | Viewed by 2663
Abstract
The role and importance of geodesists in the planning and building of civil engineering constructions are well known. However, the importance and benefits of collected data during maintenance in exploitation have arisen in the last thirty years due primarily to the development of [...] Read more.
The role and importance of geodesists in the planning and building of civil engineering constructions are well known. However, the importance and benefits of collected data during maintenance in exploitation have arisen in the last thirty years due primarily to the development of Global Positioning Systems (GPS) and Global Navigation Satellite System (GNSS) instruments, sensors and systems, which can receive signals from multiple GPS systems. In the last fifteen years, the development of Terrestrial Laser Scanners (TLS) and Image-Assisted Total Stations (IATS) has enabled much wider integration of these types of geodetic instruments with their sensors into monitoring systems for the displacement and deformation monitoring of structures, as well as for regular structure inspections. While GNSS sensors have certain limitations regarding their accuracy, their suitability in monitoring systems, and the need for a clean horizon, IATS do not have these limitations. The latest development of Total Stations (TS) called IATS is a theodolite that consists of a Robotic Total Station (RTS) with integrated image sensors. Today, IATS can be used for structural and geo-monitoring, i.e., for the determination of static and dynamic displacements and deformations, as well as for the determination of civil engineering structures’ natural frequencies. In this way, IATS can provide essential information about the current condition of structures. However, like all instruments and sensors, they have their advantages and disadvantages. IATS’s biggest advantage is their high level of accuracy and precision and the fact that they do not need to be set up on the structure, while their biggest disadvantage is that they are expensive. In this paper, the developed low-cost IATS prototype, which consists of an RTS Leica TPS1201 instrument and GoPro Hero5 camera, is presented. At first, the IATS prototype was tested in the laboratory where simulated dynamic displacements were determined. After the experiment, the IATS prototype was used in the field for the purpose of static and dynamic load testing of the railway bridge Kloštar, after its reconstruction according to HRN ISO NORM U.M1.046—Testing of bridges by load test. In this article, the determination of bridge dynamic displacements and results of the computation of natural frequencies using FFT from the measurement data obtained by means of IATS are presented. During the load testing of the bridge, the frequencies were also determined by accelerometers, and these data were used as a reference for the assessment of IATS accuracy and suitability for dynamic testing. From the conducted measurements, we successfully determined natural bridge frequencies as they match the results gained by accelerometers. Full article
(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring: Volume 2)
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18 pages, 21643 KiB  
Article
Modeling the Influence of Engine Dynamics on Its Indicator Diagram
by Piotr Deuszkiewicz, Jacek Dziurdź and Paweł Fabiś
Sensors 2021, 21(23), 7885; https://doi.org/10.3390/s21237885 - 26 Nov 2021
Cited by 4 | Viewed by 1503
Abstract
This article presents a proposal to describe the pressure changes in the combustion chamber of an engine as a function of the angle of rotation of the crankshaft, taking into account changes in rotational speed resulting from acceleration. The aim of the proposed [...] Read more.
This article presents a proposal to describe the pressure changes in the combustion chamber of an engine as a function of the angle of rotation of the crankshaft, taking into account changes in rotational speed resulting from acceleration. The aim of the proposed model is to determine variable piston forces in simulation studies of torsional vibrations of a crankshaft with a vibration damper during the acceleration process. Its essence is the use of a Fourier series as a continuous function to describe pressure changes in one cycle of work. Such a solution is required due to the variable integration step during the simulation. It was proposed to determine the series coefficients on the basis of a Fourier transform of the averaged waveform of a discreet open indicator diagram, calculated for the registration of successive cycles. Recording of the indicative pressure waveforms and shaft angle sensor signals was carried out during tests on the chassis dynamometer. An analysis of the influence of the adopted number of series coefficients on the representation of signal energy was carried out. The model can also take into account the phenomenon of work cycle uniqueness by introducing random changes in the coefficients with magnitudes set on the basis of determined standard deviations for each coefficient of the series. An indispensable supplement to the model is a description of changes in the engine rotational speed, used as a control signal for the PID controller in the simulation of the load performed by the dynamometer. The accuracy of determining the instantaneous rotational speed was analyzed on the basis of signals from the crankshaft position angle sensor and the piston top dead center (TDC) sensor. Limitations resulting from the parameters of digital signal recording were defined. Full article
(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring: Volume 2)
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22 pages, 11051 KiB  
Article
Multidimensional Data Interpretation of Vibration Signals Registered in Different Locations for System Condition Monitoring of a Three-Stage Gear Transmission Operating under Difficult Conditions
by Grzegorz Wojnar, Rafał Burdzik, Andrzej N. Wieczorek and Łukasz Konieczny
Sensors 2021, 21(23), 7808; https://doi.org/10.3390/s21237808 - 24 Nov 2021
Cited by 16 | Viewed by 2176
Abstract
This article provides a discussion of the results of studies on the original system condition monitoring of a three-stage transmission with a bevel–cylindrical–planetary configuration installed in an experimental scraper conveyor. Due to the high vibroactivity of gear transmissions operating under the impact of [...] Read more.
This article provides a discussion of the results of studies on the original system condition monitoring of a three-stage transmission with a bevel–cylindrical–planetary configuration installed in an experimental scraper conveyor. Due to the high vibroactivity of gear transmissions operating under the impact of a scraper conveyor’s chain drive, these unwanted effects of machine operating vibrations were assumed to be applied. For purposes of the study, vibrations were measured on the driving transmission housing in an idling scraper conveyor. The main purpose of the study was to establish the frequencies characteristic of the gear transmission, and to determine whether it was possible to run vibroacoustic diagnostics of the same transmission under conditions with a considerable impact of the conveyor chain. An additional cognitively significant research goal was the analysis of the dependence of the diagnostic utility of the signal depending on the sensor mounting point. Five different locations of three-axis sensors oriented to the next stages and various types of gears were determined, as well as places characterized by high spatial accessibility, which are often selected as places for measuring the vibration of gears. Using MATLAB software, a program was written that was calibrated and adapted to the specifics of the measuring equipment based on the collected test results. As a result, it was possible to obtain a multidimensional data interpretation of vibration signals of system condition monitoring of a three-stage gear transmission operating under difficult conditions. The results were based on signals registered on the real three-stage gear transmission operating under the impact of a scraper conveyor’s chain drive. Full article
(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring: Volume 2)
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21 pages, 8289 KiB  
Article
Rotating Machinery Diagnosing in Non-Stationary Conditions with Empirical Mode Decomposition-Based Wavelet Leaders Multifractal Spectra
by Iwona Komorska and Andrzej Puchalski
Sensors 2021, 21(22), 7677; https://doi.org/10.3390/s21227677 - 18 Nov 2021
Cited by 9 | Viewed by 1818
Abstract
Diagnosing the condition of rotating machines by non-invasive methods is based on the analysis of dynamic signals from sensors mounted on the machine—such as vibration, velocity, or acceleration sensors; torque meters; force sensors; pressure sensors; etc. The article presents a new method combining [...] Read more.
Diagnosing the condition of rotating machines by non-invasive methods is based on the analysis of dynamic signals from sensors mounted on the machine—such as vibration, velocity, or acceleration sensors; torque meters; force sensors; pressure sensors; etc. The article presents a new method combining the empirical mode decomposition algorithm with wavelet leader multifractal formalism applied to diagnosing damages of rotating machines in non-stationary conditions. The development of damage causes an increase in the level of multifractality of the signal. The multifractal spectrum obtained as a result of the algorithm changes its shape. Diagnosis is based on the classification of the features of this spectrum. The method is effective in relation to faults causing impulse responses in the dynamic signal registered by the sensors. The method has been illustrated with examples of vibration signals of rotating machines recorded on a laboratory stand, as well as on real objects. Full article
(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring: Volume 2)
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15 pages, 3409 KiB  
Article
An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis
by Ayaz Kafeel, Sumair Aziz, Muhammad Awais, Muhammad Attique Khan, Kamran Afaq, Sahar Ahmed Idris, Hammam Alshazly and Samih M. Mostafa
Sensors 2021, 21(22), 7587; https://doi.org/10.3390/s21227587 - 15 Nov 2021
Cited by 31 | Viewed by 3937
Abstract
Accurate and early detection of machine faults is an important step in the preventive maintenance of industrial enterprises. It is essential to avoid unexpected downtime as well as to ensure the reliability of equipment and safety of humans. In the case of rotating [...] Read more.
Accurate and early detection of machine faults is an important step in the preventive maintenance of industrial enterprises. It is essential to avoid unexpected downtime as well as to ensure the reliability of equipment and safety of humans. In the case of rotating machines, significant information about machine’s health and condition is present in the spectrum of its vibration signal. This work proposes a fault detection system of rotating machines using vibration signal analysis. First, a dataset of 3-dimensional vibration signals is acquired from large induction motors representing healthy and faulty states. The signal conditioning is performed using empirical mode decomposition technique. Next, multi-domain feature extraction is done to obtain various combinations of most discriminant temporal and spectral features from the denoised signals. Finally, the classification step is performed with various kernel settings of multiple classifiers including support vector machines, K-nearest neighbors, decision tree and linear discriminant analysis. The classification results demonstrate that a hybrid combination of time and spectral features, classified using support vector machines with Gaussian kernel achieves the best performance with 98.2% accuracy, 96.6% sensitivity, 100% specificity and 1.8% error rate. Full article
(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring: Volume 2)
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28 pages, 8330 KiB  
Article
Characterization of Dielectric Oil with a Low-Cost CMOS Imaging Sensor and a New Electric Permittivity Matrix Using the 3D Cell Method
by José Miguel Monzón-Verona, Pablo Ignacio González-Domínguez, Santiago García-Alonso and Jenifer Vaswani Reboso
Sensors 2021, 21(21), 7380; https://doi.org/10.3390/s21217380 - 06 Nov 2021
Cited by 4 | Viewed by 2215
Abstract
In this paper, a new method for characterizing the dielectric breakdown voltage of dielectric oils is presented, based on the IEC 60156 international standard. In this standard, the effective value of the dielectric breakdown voltage is obtained, but information is not provided on [...] Read more.
In this paper, a new method for characterizing the dielectric breakdown voltage of dielectric oils is presented, based on the IEC 60156 international standard. In this standard, the effective value of the dielectric breakdown voltage is obtained, but information is not provided on the distribution of Kelvin forces an instant before the dynamic behavior of the arc begins or the state of the gases that are produced an instant after the moment of appearance of the electric arc in the oil. In this paper, the behavior of the oil before and after the appearance of the electric arc is characterized by combining a low-cost CMOS imaging sensor and a new matrix of electrical permittivity associated with the dielectric oil, using the 3D cell method. In this way, we also predict the electric field before and after the electric rupture. The error compared to the finite element method is less than 0.36%. In addition, a new method is proposed to measure the kinematic viscosity of dielectric oils. Using a low-cost imaging sensor, the distribution of bubbles is measured, together with their diameters and their rates of ascent after the electric arc occurs. This method is verified using ASTM standards and data provided by the oil manufacturer. The results of these tests can be used to prevent incipient failures and evaluate preventive maintenance processes such as transformer oil replacement or recovery. Full article
(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring: Volume 2)
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16 pages, 5107 KiB  
Article
Identification of Damage on Sluice Hoist Beams Using Local Mode Evoked by Swept Frequency Excitation
by Qingyang Wei, Hao Xu, Yifei Li, Li Chen, Drahomír Novák, Li Cui and Maosen Cao
Sensors 2021, 21(19), 6357; https://doi.org/10.3390/s21196357 - 23 Sep 2021
Cited by 3 | Viewed by 1625
Abstract
As a global vibration characteristic, natural frequency often suffers from insufficient sensitivity to structural damage, which is associated with local variations of structural material or geometric properties. Such a drawback is particularly significant when dealing with the large scale and complexity of sluice [...] Read more.
As a global vibration characteristic, natural frequency often suffers from insufficient sensitivity to structural damage, which is associated with local variations of structural material or geometric properties. Such a drawback is particularly significant when dealing with the large scale and complexity of sluice structural systems. To this end, a damage detection method in sluice hoist beams is proposed that relies on the utilization of the local primary frequency (LPF), which is obtained based on the swept frequency excitation (SFE) technique and local resonance response band (LRRB) selection. Using this method, the local mode of the target sluice hoist beam can be effectively excited, while the vibrations of other components in the system are suppressed. As a result, the damage will cause a significant shift in the LPF of the sluice hoist beam at the local mode. A damage index was constructed to quantitatively reflect the damage degree of the sluice hoist beam. The accuracy and reliability of the proposed method were verified on a three-dimensional finite element model of a sluice system, with the noise resistance increased from 0.05 to 0.2 based on the hammer impact method. The proposed method exhibits promising potential for damage detection in complex structural systems. Full article
(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring: Volume 2)
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22 pages, 3826 KiB  
Article
Development of a Low-Cost System for the Accurate Measurement of Structural Vibrations
by Seyedmilad Komarizadehasl, Behnam Mobaraki, Haiying Ma, Jose-Antonio Lozano-Galant and Jose Turmo
Sensors 2021, 21(18), 6191; https://doi.org/10.3390/s21186191 - 15 Sep 2021
Cited by 34 | Viewed by 4854
Abstract
Nowadays, engineers are widely using accelerometers to record the vibration of structures for structural verification purposes. The main obstacle for using these data acquisition systems is their high cost, which limits its use to unique structures with a relatively high structural health monitoring [...] Read more.
Nowadays, engineers are widely using accelerometers to record the vibration of structures for structural verification purposes. The main obstacle for using these data acquisition systems is their high cost, which limits its use to unique structures with a relatively high structural health monitoring budget. In this paper, a Cost Hyper-Efficient Arduino Product (CHEAP) has been developed to accurately measure structural accelerations. CHEAP is a system that is composed of five low-cost accelerometers that are connected to an Arduino microcontroller as their data acquisition system. Test results show that CHEAP not only has a significantly lower price (14 times cheaper in the worst-case scenario) compared with other systems used for comparison but also shows better accuracy on low frequencies for low acceleration amplitudes. Moreover, the final output results of Fast Fourier Transformation (FFT) assessments showed a better observable resolution for CHEAP than the studied control systems. Full article
(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring: Volume 2)
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15 pages, 5854 KiB  
Article
Identification of Multiple Cracks in Composite Laminated Beams Using Perturbation to Dynamic Equilibrium
by Aimin Deng, Maosen Cao, Qitian Lu and Wei Xu
Sensors 2021, 21(18), 6171; https://doi.org/10.3390/s21186171 - 15 Sep 2021
Cited by 4 | Viewed by 1979
Abstract
Identification of cracks in beam-type components is significant to ensure the safety of structures. Among the approaches relying on mode shapes, the concept of transverse pseudo-force (TPF) has been well proved for single and multiple crack identification in beams made of isotropic materials; [...] Read more.
Identification of cracks in beam-type components is significant to ensure the safety of structures. Among the approaches relying on mode shapes, the concept of transverse pseudo-force (TPF) has been well proved for single and multiple crack identification in beams made of isotropic materials; however, there is a noticeable gap between the concept of TPF and its applications in composite laminated beams. To fill this gap, an enhanced TPF approach that relies on perturbation to dynamic equilibrium is proposed for the identification of multiple cracks in composite laminated beams. Starting from the transverse equation of motion, this study formulates the TPF in a composite laminated beam for the identification of multiple cracks. The capability of the approach is numerically verified using the FE method. The applicability of the approach is experimentally validated on a carbon fiber-reinforced polymer laminated beam with three cracks, the mode shapes of which are acquired through non-contact vibration measurement using a scanning laser vibrometer. In particular, a statistic manner is utilized to enable the approach to be feasible to real scenarios in the absence of material and structural information; besides, an integrating scheme is utilized to enable the approach to be capable of identifying cracks even in the vicinity of nodes of mode shapes. Full article
(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring: Volume 2)
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Review

Jump to: Research

31 pages, 2121 KiB  
Review
Tool Condition Monitoring for High-Performance Machining Systems—A Review
by Ayman Mohamed, Mahmoud Hassan, Rachid M’Saoubi and Helmi Attia
Sensors 2022, 22(6), 2206; https://doi.org/10.3390/s22062206 - 12 Mar 2022
Cited by 45 | Viewed by 7645
Abstract
In the era of the “Industry 4.0” revolution, self-adjusting and unmanned machining systems have gained considerable interest in high-value manufacturing industries to cope with the growing demand for high productivity, standardized part quality, and reduced cost. Tool condition monitoring (TCM) systems pave the [...] Read more.
In the era of the “Industry 4.0” revolution, self-adjusting and unmanned machining systems have gained considerable interest in high-value manufacturing industries to cope with the growing demand for high productivity, standardized part quality, and reduced cost. Tool condition monitoring (TCM) systems pave the way for automated machining through monitoring the state of the cutting tool, including the occurrences of wear, cracks, chipping, and breakage, with the aim of improving the efficiency and economics of the machining process. This article reviews the state-of-the-art TCM system components, namely, means of sensing, data acquisition, signal conditioning and processing, and monitoring models, found in the recent open literature. Special attention is given to analyzing the advantages and limitations of current practices in developing wireless tool-embedded sensor nodes, which enable seamless implementation and Industrial Internet of Things (IIOT) readiness of TCM systems. Additionally, a comprehensive review of the selection of dimensionality reduction techniques is provided due to the lack of clear recommendations and shortcomings of various techniques developed in the literature. Recent attempts for TCM systems’ generalization and enhancement are discussed, along with recommendations for possible future research avenues to improve TCM systems accuracy, reliability, functionality, and integration. Full article
(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring: Volume 2)
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21 pages, 8946 KiB  
Review
Motion Magnification of Vibration Image in Estimation of Technical Object Condition-Review
by Michał Śmieja, Jarosław Mamala, Krzysztof Prażnowski, Tomasz Ciepliński and Łukasz Szumilas
Sensors 2021, 21(19), 6572; https://doi.org/10.3390/s21196572 - 30 Sep 2021
Cited by 10 | Viewed by 3573
Abstract
One of the most important features of the proper operation of technical objects is monitoring the vibrations of their mechanical components. The currently significant proportion of the research methods in this regard includes a group of research methods based on the conversion of [...] Read more.
One of the most important features of the proper operation of technical objects is monitoring the vibrations of their mechanical components. The currently significant proportion of the research methods in this regard includes a group of research methods based on the conversion of vibrations using sensors providing data from individual locations. In parallel with the continuous improvement of these tools, new methods for acquiring information on the condition of the object have emerged due to the rapid development of visual systems. Their actual effectiveness determined the switch from research laboratories to actual industrial installations. In many cases, the application of the visualization methods can supplement the conventional methods applied and, under particular conditions, can effectively replace them. The decisive factor is their non-contact nature and the possibility for simultaneous observation of multiple points of the selected area. Visual motion magnification (MM) is an image processing method that involves the conscious and deliberate deformation of input images to the form that enables the visual observation of vibration processes which are not visible in their natural form. The first part of the article refers to the basic terms in the field of expressing motion in an image (based on the Lagrangian and Eulerian approaches), the formulation of the term of optical flow (OF), and the interpretation of an image in time and space. The following part of the article reviews the main processing algorithms in the aspect of computational complexity and visual quality and their modification for applications under specific conditions. The comparison of the MM methods presented in the paper and recommendations for their applications across a wide variety of fields were supported with examples originating from recent publications. The effectiveness of visual methods based on motion magnification in machine diagnosis and the identification of malfunctions are illustrated with selected examples of the implementation derived from authors’ workshop practice under industrial conditions. Full article
(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring: Volume 2)
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