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Machine Health Monitoring and Fault Diagnosis Techniques
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Machine Health Monitoring and Fault Diagnosis Techniques

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

Deadline for manuscript submissions: closed (20 November 2022) | Viewed by 39474

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Shilong Sun

E-Mail Website
Guest Editor
Harbin Institute of Technology, Shenzhen, China
Interests: vibration energy harvesting design; fault diagnosis and prognosis; decision-making with artificial intelligence; deep learning for industrial data
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Changqing Shen

E-Mail Website
Guest Editor
School of Rail Transportation, Soochow University, Suzhou 215131, China
Interests: signal processing; data mining; fault diagnosis; mechanical engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Dong Wang

E-Mail Website
Guest Editor
The State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: intelligent operation and maintenance; mathematical basis of fault feature extraction and sparse measure; prognostic and health management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine health monitoring and fault diagnosis have recently come to play a crucial role in automatic and intelligent industrial plants. Intelligent fault diagnosis has been proposed based on machine learning, deep learning, and artificial intelligence. However, there are still several issues that require further investigation—with intelligent fault diagnosis methodologies being among them, e.g.,  early fault detection features, the few-shot sample machine learning algorithm, data augmentation techniques for deep learning, data fusion method for domain adaptation, feature representation with self-supervision, and interpretable deep learning algorithms.

This Special Issue aims to highlight the state-of-the-art techniques used for machine health monitoring and fault diagnosis, especially for intelligent fault diagnosis algorithm development, fault feature extraction, and intelligent machine monitoring. Topics include but are not limited to:

  • Rotational machine monitoring and vibration signal processing
  • Intelligent early fault detection and diagnosis
  • Few-shot sample learning for fault detection
  • Feature construction with intelligent algorithms
  • Data-efficient domain adaptation and transfer learning
  • Interpretable deep learning for fault diagnosis
  • Data augmentation techniques for fault diagnosis
  • Sensor data fusion for fault diagnosis
  • Measurement methods, technologies, and systems for fault diagnosis

Dr. Shilong Sun
Prof. Dr. Changqing Shen
Dr. Dong Wang
Guest Editors

Manuscript Submission Information

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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

  • rotational machine monitoring and vibration signal processing
  • intelligent early fault detection and diagnosis
  • few-shot sample learning for fault detection
  • feature construction with intelligent algorithms
  • data-efficient domain adaptation and transfer learning
  • interpretable deep learning for fault diagnosis
  • data augmentation techniques for fault diagnosis
  • sensor data fusion for fault diagnosis
  • measurement methods, technologies, and systems for fault diagnosis

Published Papers (18 papers)

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Editorial

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4 pages, 191 KiB  
Editorial
Editorial for Special Issue: Machine Health Monitoring and Fault Diagnosis Techniques
by Shilong Sun, Changqing Shen and Dong Wang
Sensors 2023, 23(7), 3493; https://doi.org/10.3390/s23073493 - 27 Mar 2023
Cited by 1 | Viewed by 1038
Abstract
Machine health monitoring and fault diagnosis have played crucial roles in automatic and intelligent industrial plants [...] Full article
(This article belongs to the Special Issue Machine Health Monitoring and Fault Diagnosis Techniques)

Research

Jump to: Editorial

25 pages, 4001 KiB  
Article
Automated Operational Modal Analysis for Rotating Machinery Based on Clustering Techniques
by Nathali Rolon Dreher, Gustavo Chaves Storti and Tiago Henrique Machado
Sensors 2023, 23(3), 1665; https://doi.org/10.3390/s23031665 - 02 Feb 2023
Cited by 4 | Viewed by 1417
Abstract
Many parameters can be used to express a machine’s condition and to track its evolution through time, such as modal parameters extracted from vibration signals. Operational Modal Analysis (OMA), commonly used to extract modal parameters from systems under operating conditions, was successfully employed [...] Read more.
Many parameters can be used to express a machine’s condition and to track its evolution through time, such as modal parameters extracted from vibration signals. Operational Modal Analysis (OMA), commonly used to extract modal parameters from systems under operating conditions, was successfully employed in many monitoring systems, but its application in rotating machinery is still in development due to the distinct characteristics of this system. To implement efficient monitoring systems based on OMA, it is essential to automatically extract the modal parameters, which several studies have proposed in the literature. However, these algorithms are usually developed to deal with structures that have different characteristics when compared to rotating machinery, and, therefore, work poorly or do not work with this kind of system. Thus, this paper proposes, and has as its main novelty in, a new automated algorithm to carry out modal parameter identification on rotating machinery through OMA. The proposed technique was applied in two different datasets to enable the evaluation of the robustness to different systems and test conditions. It is revealed that the proposed algorithm is suitable for the accurate extraction of frequencies and damping ratios from the stabilization diagram, for both the rotor and the foundation, and only one user defined parameter is required. Full article
(This article belongs to the Special Issue Machine Health Monitoring and Fault Diagnosis Techniques)
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20 pages, 5528 KiB  
Article
Rolling Bearing Performance Degradation Assessment with Adaptive Sensitive Feature Selection and Multi-Strategy Optimized SVDD
by Zhengjiang Feng, Zhihai Wang, Xiaoqin Liu and Jiahui Li
Sensors 2023, 23(3), 1110; https://doi.org/10.3390/s23031110 - 18 Jan 2023
Cited by 2 | Viewed by 1314
Abstract
In light of the problems of a single vibration feature containing limited information on the degradation of rolling bearings, the redundant information in high-dimensional feature sets inaccurately reflecting the reliability of rolling bearings in service, and assessments of the degradation performance being disturbed [...] Read more.
In light of the problems of a single vibration feature containing limited information on the degradation of rolling bearings, the redundant information in high-dimensional feature sets inaccurately reflecting the reliability of rolling bearings in service, and assessments of the degradation performance being disturbed by outliers and false fluctuations in the signal, this study proposes a method of assessing rolling bearings’ performance in terms of degradation using adaptive sensitive feature selection and multi-strategy optimized support vector data description (SVDD). First, a high-dimensional feature set of vibration signals from rolling bearings was extracted. Second, a method combining the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and K-medoids was used to comprehensively evaluate the features with multiple evaluation indicators and to adaptively select better degradation features to construct the sensitive feature set. Next, multi-strategy optimization of the SVDD model was carried out by introducing the autocorrelation kernel regression (AAKR) and a multi-kernel function to improve the ability of the evaluation model to overcome outliers and false fluctuations. Through validation, it could be seen that the method in this study uses samples of rolling bearings in the healthy early stage to establish the evaluation model, which can adaptively determine the starting point of the bearing’s degradation. The stability and accuracy of the model were effectively improved. Full article
(This article belongs to the Special Issue Machine Health Monitoring and Fault Diagnosis Techniques)
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32 pages, 8667 KiB  
Article
Fault Monitoring Based on the VLSW-MADF Test and DLPPCA for Multimodal Processes
by Shu Wang, Yicheng Wang, Jiarong Tong and Yuqing Chang
Sensors 2023, 23(2), 987; https://doi.org/10.3390/s23020987 - 14 Jan 2023
Cited by 3 | Viewed by 1355
Abstract
Actual industrial processes often exhibit multimodal characteristics, and their data exhibit complex features, such as being dynamic, nonlinear, multimodal, and strongly coupled. Although many modeling approaches for process fault monitoring have been proposed in academia, due to the complexity of industrial data, challenges [...] Read more.
Actual industrial processes often exhibit multimodal characteristics, and their data exhibit complex features, such as being dynamic, nonlinear, multimodal, and strongly coupled. Although many modeling approaches for process fault monitoring have been proposed in academia, due to the complexity of industrial data, challenges remain. Based on the concept of multimodal modeling, this paper proposes a multimodal process monitoring method based on the variable-length sliding window-mean augmented Dickey–Fuller (VLSW-MADF) test and dynamic locality-preserving principal component analysis (DLPPCA). In the offline stage, considering the fluctuation characteristics of data, the trend variables of data are extracted and input into VLSW-MADF for modal identification, and different modalities are modeled separately using DLPPCA. In the online monitoring phase, the previous moment’s historical modal information is fully utilized, and modal identification is performed only when necessary to reduce computational cost. Finally, the proposed method is validated to be accurate and effective for modal identification, modeling, and online monitoring of multimodal processes in TE simulation and actual plant data. The proposed method improves the fault detection rate of multimodal process fault monitoring by about 14% compared to the classical DPCA method. Full article
(This article belongs to the Special Issue Machine Health Monitoring and Fault Diagnosis Techniques)
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23 pages, 7163 KiB  
Article
An Imbalanced Fault Diagnosis Method Based on TFFO and CNN for Rotating Machinery
by Long Zhang, Yangyuan Liu, Jianmin Zhou, Muxu Luo, Shengxin Pu and Xiaotong Yang
Sensors 2022, 22(22), 8749; https://doi.org/10.3390/s22228749 - 12 Nov 2022
Cited by 7 | Viewed by 1351
Abstract
Deep learning-based fault diagnosis usually requires a rich supply of data, but fault samples are scarce in practice, posing a considerable challenge for existing diagnosis approaches to achieve highly accurate fault detection in real applications. This paper proposes an imbalanced fault diagnosis of [...] Read more.
Deep learning-based fault diagnosis usually requires a rich supply of data, but fault samples are scarce in practice, posing a considerable challenge for existing diagnosis approaches to achieve highly accurate fault detection in real applications. This paper proposes an imbalanced fault diagnosis of rotatory machinery that combines time-frequency feature oversampling (TFFO) with a convolutional neural network (CNN). First, the sliding segmentation sampling method is employed to primarily increase the number of fault samples in the form of one-dimensional signals. Immediately after, the signals are converted into two-dimensional time-frequency feature maps by continuous wavelet transform (CWT). Subsequently, the minority samples are expanded again using the synthetic minority oversampling technique (SMOTE) to realize TFFO. After such two-fold data expansion, a balanced data set is obtained and imported to an improved 2dCNN based on the LeNet-5 to implement fault diagnosis. In order to verify the proposed method, two experiments involving single and compound faults are conducted on locomotive wheel-set bearings and a gearbox, resulting in several datasets with different imbalanced degrees and various signal-to-noise ratios. The results demonstrate the advantages of the proposed method in terms of classification accuracy and stability as well as noise robustness in imbalanced fault diagnosis, and the fault classification accuracy is over 97%. Full article
(This article belongs to the Special Issue Machine Health Monitoring and Fault Diagnosis Techniques)
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16 pages, 3700 KiB  
Article
Transient Thermal Analysis Model of Damaged Bearing Considering Thermo-Solid Coupling Effect
by Yali Sun, Chong Zhang, Xing Zhao, Xiaodong Liu, Chang Lu and Jiyou Fei
Sensors 2022, 22(21), 8171; https://doi.org/10.3390/s22218171 - 25 Oct 2022
Cited by 3 | Viewed by 1313
Abstract
As one of the important parameters of bearing operation, temperature is a key metric to diagnose the state of service of a bearing. However, there are still some shortcomings in the study of the temperature variation law for damaged bearings. In this paper, [...] Read more.
As one of the important parameters of bearing operation, temperature is a key metric to diagnose the state of service of a bearing. However, there are still some shortcomings in the study of the temperature variation law for damaged bearings. In this paper, according to the structural characteristics of bearings, the influence law of thermal-solid coupling effect on bearing structure is considered, and a novel transient temperature analysis model of damaged bearings is established. First, a quasi-static analysis of the bearing is performed to obtain the variation laws of the key parameters of the bearing under thermal expansion. Then, the load variation law of the bearing under the condition of damage is discussed, and the heat generation and heat transfer of the damaged bearing during operation are studied. Based on the thermal grid method, a transient temperature analysis model of the damaged bearing is developed. Finally, the model is tested experimentally and the influence of the rotate speed and load on the bearing temperature variation is analyzed. The results show that the established model can effectively predict the temperature variation and thermal equilibrium temperature of damaged bearings. Full article
(This article belongs to the Special Issue Machine Health Monitoring and Fault Diagnosis Techniques)
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19 pages, 2557 KiB  
Article
Multiple Sensor Fault Detection Using Index-Based Method
by Daijiry Narzary and Kalyana Chakravarthy Veluvolu
Sensors 2022, 22(20), 7988; https://doi.org/10.3390/s22207988 - 19 Oct 2022
Cited by 2 | Viewed by 6582
Abstract
The research on sensor fault detection has drawn much interest in recent years. Abrupt, incipient, and intermittent sensor faults can cause the complete blackout of the system if left undetected. In this research, we examined the observer-based residual analysis via index-based approaches for [...] Read more.
The research on sensor fault detection has drawn much interest in recent years. Abrupt, incipient, and intermittent sensor faults can cause the complete blackout of the system if left undetected. In this research, we examined the observer-based residual analysis via index-based approaches for fault detection of multiple sensors in a healthy drive. Seven main indices including the moving mean, average, root mean square, energy, variance, first-order derivative, second-order derivative, and auto-correlation-based index were employed and analyzed for sensor fault diagnosis. In addition, an auxiliary index was computed to differentiate a faulty sensor from a non-faulty one. These index-based methods were utilized for further analysis of sensor fault detection operating under a range of various loads, varying speeds, and fault severity levels. The simulation results on a permanent magnet synchronous motor (PMSM) are provided to demonstrate the pros and cons of various index-based methods for various fault detection scenarios. Full article
(This article belongs to the Special Issue Machine Health Monitoring and Fault Diagnosis Techniques)
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21 pages, 11062 KiB  
Article
Cluster Migration Distance for Performance Degradation Assessment of Water Pump Bearings
by Zhongping Zhai, Zihao Zhu, Yifan Xu, Xinhang Zhao, Fang Liu and Zhihua Feng
Sensors 2022, 22(18), 6809; https://doi.org/10.3390/s22186809 - 08 Sep 2022
Cited by 1 | Viewed by 1371
Abstract
Because the signal of water pump bearing is seriously disturbed by noise and the fault evolution is complex, it is difficult to describe the performance degradation trend of water pump bearing in a timely and accurate manner using the traditional performance degradation index [...] Read more.
Because the signal of water pump bearing is seriously disturbed by noise and the fault evolution is complex, it is difficult to describe the performance degradation trend of water pump bearing in a timely and accurate manner using the traditional performance degradation index (PDI). In this paper, a new Cluster Migration Distance (CMD) algorithm is proposed. The extraction of the indicator includes the following four steps: First, the relevant blind separation is used to extract the useful signal of the monitored bearing from the mixed signal; secondly, the impact component is further enhanced by wavelet packet analysis. Then, the redundancy of the original feature vectors is eliminated using our previously proposed KJADE (Kernel Joint Approximate Diagonalization of Eigen-matrices) method. Finally, the newly proposed CMD index is computed as PDI. By calculating the offset trajectory of the feature cluster centroid in the continuous running process of the bearing, CMD can aptly deal with the complex and variable features in the fault evolution process of the water pump bearing. The whole-life monitoring data of a 220 KW water pump system are processed. The results show that the proposed CMD index has better early-warning ability and monotonicity than the traditional kurtosis index. Full article
(This article belongs to the Special Issue Machine Health Monitoring and Fault Diagnosis Techniques)
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28 pages, 12412 KiB  
Article
Rolling Bearing Fault Diagnosis Based on WGWOA-VMD-SVM
by Junbo Zhou, Maohua Xiao, Yue Niu and Guojun Ji
Sensors 2022, 22(16), 6281; https://doi.org/10.3390/s22166281 - 21 Aug 2022
Cited by 39 | Viewed by 3453
Abstract
A rolling bearing fault diagnosis method based on whale gray wolf optimization algorithm-variational mode decomposition-support vector machine (WGWOA-VMD-SVM) was proposed to solve the unclear fault characterization of rolling bearing vibration signal due to its nonlinear and nonstationary characteristics. A whale gray wolf optimization [...] Read more.
A rolling bearing fault diagnosis method based on whale gray wolf optimization algorithm-variational mode decomposition-support vector machine (WGWOA-VMD-SVM) was proposed to solve the unclear fault characterization of rolling bearing vibration signal due to its nonlinear and nonstationary characteristics. A whale gray wolf optimization algorithm (WGWOA) was proposed by combining whale optimization algorithm (WOA) and gray wolf optimization (GWO), and the rolling bearing signal was decomposed by using variational mode decomposition (VMD). Each eigenvalue was extracted as eigenvector after VMD, and the training and test sets of the fault diagnosis model were divided accordingly. The support vector machine (SVM) was used as the fault diagnosis model and optimized by using WGWOA. The validity of this method was verified by two cases of Case Western Reserve University bearing data set and laboratory test. The test results show that in the bearing data set of Case Western Reserve University, compared with the existing VMD-SVM method, the fault diagnosis accuracy rate of the WGWOA-VMD-SVM method in five repeated tests reaches 100.00%, which preliminarily verifies the feasibility of this algorithm. In the laboratory test case, the diagnostic effect of the proposed fault diagnosis method is compared with backpropagation neural network, SVM, VMD-SVM, WOA-VMD-SVM, GWO-VMD-SVM, and WGWOA-VMD-SVM. Test results show that the accuracy rate of WGWOA-VMD-SVM fault diagnosis is the highest, the accuracy rate of a single test reaches 100.00%, and the accuracy rate of five repeated tests reaches 99.75%, which is the highest compared with the above six methods. WGWOA plays a good optimization role in optimizing VMD and SVM. The signal decomposed by VMD is optimized by using the WGWOA algorithm without mode overlap. WGWOA has the better convergence performance than WOA and GWO, which further verifies its superiority among the compared methods. The research results can provide an effective improvement method for the existing rolling bearing fault diagnosis technology. Full article
(This article belongs to the Special Issue Machine Health Monitoring and Fault Diagnosis Techniques)
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15 pages, 1451 KiB  
Article
Miniterm, a Novel Virtual Sensor for Predictive Maintenance for the Industry 4.0 Era
by Eduardo Garcia, Nicolás Montés, Javier Llopis and Antonio Lacasa
Sensors 2022, 22(16), 6222; https://doi.org/10.3390/s22166222 - 19 Aug 2022
Cited by 15 | Viewed by 2026
Abstract
This article introduces a novel virtual sensor for predictive maintenance called mini-term. A mini-term can be defined as the time it takes for a part of the machine to do its job. Being a technical sub-cycle time, its function has been linked to [...] Read more.
This article introduces a novel virtual sensor for predictive maintenance called mini-term. A mini-term can be defined as the time it takes for a part of the machine to do its job. Being a technical sub-cycle time, its function has been linked to production. However, when a machine or component gets deteriorated, the mini-term also suffers deterioration, allowing it to be a multifunctional indicator for the prediction of machine failures as well as measurement of production. Currently, in Industry 4.0, one of the handicaps is Big Data and Data Analysis. However, in the case of predictive maintenance, the need to install sensors in the machines means that when the proposed scientific solutions reach the industry, they cannot be carried out massively due to the high cost this entails. The advantage introduced by the mini-term is that it can be implemented in an easy and simple way in pre-installed systems since you only need to program a timer in the PLC or PC that controls the line/machine in the production line, allowing, according to the authors’ knowledge, to build industrial Big Data on predictive maintenance for the first time, which is called Miniterm 4.0. This article shows evidence of the important improvements generated by the use of Miniterm 4.0 in a factory. At the end of the paper we show the evolution of TAV (Technical availability), Mean Time To Repair (MTTR), EM (Number of Work order (Emergency Orders/line Stop)) and OM (Labour hours in EM) showing a very important improvement as the number of mini-terms was increased and the Miniterm 4.0 system became more reliable. In particular, TAV is increased by 15%, OM is reduced in 5000 orders, MTTR is reduced in 2 h and there are produced 3000 orders less than when mini-terms did not exist. At the end of the article we discuss the benefits and limitations of the mini-terms and we show the conclusions and future works. Full article
(This article belongs to the Special Issue Machine Health Monitoring and Fault Diagnosis Techniques)
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13 pages, 4412 KiB  
Article
A Novel Method of Impeller Blade Monitoring Using Shaft Vibration Signal Processing
by Jindrich Liska, Vojtech Vasicek and Jan Jakl
Sensors 2022, 22(13), 4932; https://doi.org/10.3390/s22134932 - 29 Jun 2022
Cited by 2 | Viewed by 1237
Abstract
The monitoring of impeller blade vibrations is an important task in the diagnosis of turbomachinery, especially in terms of steam turbines. Early detection of potential faults is the key to avoid the risk of turbine unexpected outages and to minimize profit loss. One [...] Read more.
The monitoring of impeller blade vibrations is an important task in the diagnosis of turbomachinery, especially in terms of steam turbines. Early detection of potential faults is the key to avoid the risk of turbine unexpected outages and to minimize profit loss. One of the ways to achieve this is long-term monitoring. However, existing monitoring systems for impeller blade long-term monitoring are quite expensive and also require special sensors to be installed. It is even common that the impeller blades are not monitored at all. In recent years, the authors of this paper developed a new method of impeller blade monitoring that is based on relative shaft vibration signal measurement and analysis. In this case, sensors that are already standardly installed in the bearing pedestal are used. This is a significant change in the accessibility of blade monitoring for a steam turbine operator in terms of expenditures. This article describes the developed algorithm for the relative shaft vibration signal analysis that is designed to run in a long-term perspective as a part of a remote monitoring system to track the natural blade frequency and its amplitude automatically. Full article
(This article belongs to the Special Issue Machine Health Monitoring and Fault Diagnosis Techniques)
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18 pages, 3643 KiB  
Article
A Robust Deep Neural Network for Rolling Element Fault Diagnosis under Various Operating and Noisy Conditions
by Chun-Yao Lee, Guang-Lin Zhuo and Truong-An Le
Sensors 2022, 22(13), 4705; https://doi.org/10.3390/s22134705 - 22 Jun 2022
Cited by 9 | Viewed by 1514
Abstract
This study proposes a new intelligent diagnostic method for bearing faults in rotating machinery. The method uses a combination of nonlinear mode decomposition based on the improved fast kurtogram, gramian angular field, and convolutional neural network to detect the bearing state of rotating [...] Read more.
This study proposes a new intelligent diagnostic method for bearing faults in rotating machinery. The method uses a combination of nonlinear mode decomposition based on the improved fast kurtogram, gramian angular field, and convolutional neural network to detect the bearing state of rotating machinery. The nonlinear mode decomposition based on the improved fast kurtogram inherits the advantages of the original algorithm while improving the computational efficiency and signal-to-noise ratio. The gramian angular field can construct a two-dimensional image without destroying the time relationship of the signal. Therefore, the proposed method can perform fault diagnosis on rotating machinery under complex operating conditions. The proposed method is verified on the Paderborn dataset under heavy noise and multiple operating conditions to evaluate its effectiveness. Experimental results show that the proposed model outperforms wavelet denoising and the traditional adaptive decomposition method. The proposed model achieves over 99.6% accuracy in all four operating conditions provided by this dataset, and 93.8% accuracy in a strong noise environment with a signal-to-noise ratio of −4 dB. Full article
(This article belongs to the Special Issue Machine Health Monitoring and Fault Diagnosis Techniques)
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13 pages, 1497 KiB  
Article
Implementation of Aging Mechanism Analysis and Prediction for XILINX 7-Series FPGAs with a 28-nm Process
by Zeyu Li, Zhao Huang, Quan Wang, Junjie Wang and Nan Luo
Sensors 2022, 22(12), 4439; https://doi.org/10.3390/s22124439 - 12 Jun 2022
Cited by 3 | Viewed by 1830
Abstract
Commercial off-the-shelf (COTS) field-programmable gate arrays (FPGAs) with a 28-nm process have become popular devices for computing systems. Although current generation FPGAs have advantages over previous models, the phenomenon of circuit aging has become more significant with the sharp reduction in the process [...] Read more.
Commercial off-the-shelf (COTS) field-programmable gate arrays (FPGAs) with a 28-nm process have become popular devices for computing systems. Although current generation FPGAs have advantages over previous models, the phenomenon of circuit aging has become more significant with the sharp reduction in the process size of FPGAs. Aging results in FPGA performance degradation over time and, ultimately, hard faults. However, few studies have focused on understanding aging mechanisms or estimating the aging trend of 28-nm FPGAs. For this, we used a ring oscillator (RO)-based test structure to extract data and build a dataset that could be used to predict aging trends and determine the primary aging mechanisms of 28-nm FPGAs. Moreover, we proposed a correction method to correct temperature-induced measurement errors in accelerated tests. Furthermore, we employed four machine learning (ML) technologies that were based on accurate measurement datasets to predict FPGA aging trends. In the experiment, 24 XILINX 7-series FPGAs (28 nm) were evaluated for 10+ years of circuit operation using accelerated tests. The results showed that the aging effects of negative-bias temperature instability (NBTI) was the primary aging mechanism. The correction method proposed in this paper could effectively eliminate measurement errors. In addition, the minimum prediction error rate of the ML model was only 0.292%. Full article
(This article belongs to the Special Issue Machine Health Monitoring and Fault Diagnosis Techniques)
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14 pages, 3680 KiB  
Article
An Unsupervised Condition Monitoring System for Electrode Milling Problems in the Resistance Welding Process
by Daniel Ibáñez, Eduardo Garcia, Jesús Soret and Julio Martos
Sensors 2022, 22(12), 4311; https://doi.org/10.3390/s22124311 - 07 Jun 2022
Cited by 3 | Viewed by 1638
Abstract
Resistance spot welding is one of the most widely used metal joining processes in the manufacturing industry, used for structural body manufacturing, railway vehicle construction, electronics manufacturing, battery manufacturing, etc. Due to its wide use, the quality of welded joints is of great [...] Read more.
Resistance spot welding is one of the most widely used metal joining processes in the manufacturing industry, used for structural body manufacturing, railway vehicle construction, electronics manufacturing, battery manufacturing, etc. Due to its wide use, the quality of welded joints is of great importance to the manufacturing industry, as it is critical for ensuring the integrity of finished products, such as car bodies, that withstand high levels of stress. The quality of the welding is influenced both by the programming of the welding and by the good condition of the mechanical part that carries out the welding. These mechanical factors, such as electrode geometry and wear, degrade over time. As the welding points are made, the geometry and properties of the electrodes change, so they undergo a milling process to remove impurities and return them to their initial geometry. Sometimes the milling is deficient, and the electrode continues to wear, causing welding problems such as loose spots and metal spatter. This article presents a method for condition monitoring of the milling process and weld wear based on existing data in real production lines. The use of unsupervised clustering methods is proposed to perform a check by which, using current and resistance data, the electrode wear is grouped. Specifically, a method using multidimensional k-means for the condition monitoring of electrode wear is established. This research gives a real and applicable solution for reducing the quality problems caused by milling defects and electrode wear in the production lines of high-production manufacturing industries, presenting a system for sending alarms based on the behavior of welding variables. Full article
(This article belongs to the Special Issue Machine Health Monitoring and Fault Diagnosis Techniques)
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15 pages, 6392 KiB  
Article
Rolling Bearing Fault Diagnosis Based on Markov Transition Field and Residual Network
by Jialin Yan, Jiangming Kan and Haifeng Luo
Sensors 2022, 22(10), 3936; https://doi.org/10.3390/s22103936 - 23 May 2022
Cited by 24 | Viewed by 2774
Abstract
Data-driven rolling-bearing fault diagnosis methods are mostly based on deep-learning models, and their multilayer nonlinear mapping capability can improve the accuracy of intelligent fault diagnosis. However, problems such as gradient disappearance occur as the number of network layers increases. Moreover, directly taking the [...] Read more.
Data-driven rolling-bearing fault diagnosis methods are mostly based on deep-learning models, and their multilayer nonlinear mapping capability can improve the accuracy of intelligent fault diagnosis. However, problems such as gradient disappearance occur as the number of network layers increases. Moreover, directly taking the raw vibration signals of rolling bearings as the network input results in incomplete feature extraction. In order to efficiently represent the state characteristics of vibration signals in image form and improve the feature learning capability of the network, this paper proposes fault diagnosis model MTF-ResNet based on a Markov transition field and deep residual network. First, the data of raw vibration signals are augmented by using a sliding window. Then, vibration signal samples are converted into two-dimensional images by MTF, which retains the time dependence and frequency structure of time-series signals, and a deep residual neural network is established to perform feature extraction, and identify the severity and location of the bearing faults through image classification. Lastly, experiments were conducted on a bearing dataset to verify the effectiveness and superiority of the MTF-ResNet model. Features learned by the model are visualized by t-SNE, and experimental results indicate that MTF-ResNet showed better average accuracy compared with several widely used diagnostic methods. Full article
(This article belongs to the Special Issue Machine Health Monitoring and Fault Diagnosis Techniques)
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22 pages, 9543 KiB  
Article
Rolling Bearing Fault Diagnosis Based on Successive Variational Mode Decomposition and the EP Index
by Yuanjing Guo, Youdong Yang, Shaofei Jiang, Xiaohang Jin and Yanding Wei
Sensors 2022, 22(10), 3889; https://doi.org/10.3390/s22103889 - 20 May 2022
Cited by 11 | Viewed by 1890
Abstract
Rolling bearing is an important part guaranteeing the normal operation of rotating machinery, which is also prone to various damages due to severe running conditions. However, it is usually difficult to extract the weak fault characteristic information from rolling bearing vibration signals and [...] Read more.
Rolling bearing is an important part guaranteeing the normal operation of rotating machinery, which is also prone to various damages due to severe running conditions. However, it is usually difficult to extract the weak fault characteristic information from rolling bearing vibration signals and to realize a rolling bearing fault diagnosis. Hence, this paper offers a rolling bearing fault diagnosis method based on successive variational mode decomposition (SVMD) and the energy concentration and position accuracy (EP) index. Since SVMD decomposes a vibration signal of a rolling bearing into a number of modes, it is difficult to select the target mode with the ideal fault characteristic information. Comprehensively considering the energy concentration degree and frequency position accuracy of the fault characteristic component, the EP index is proposed to indicate the target mode. As the balancing parameter is crucial to the performance of SVMD and must be set properly, the line search method guided by the EP index is introduced to determine an optimal value for the balancing parameter of SVMD. The simulation and experiment results demonstrate that the proposed SVMD method is effective for rolling bearing fault diagnosis and superior to the variational mode decomposition (VMD) method. Full article
(This article belongs to the Special Issue Machine Health Monitoring and Fault Diagnosis Techniques)
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20 pages, 4174 KiB  
Article
Compression Reconstruction and Fault Diagnosis of Diesel Engine Vibration Signal Based on Optimizing Block Sparse Bayesian Learning
by Huajun Bai, Liang Wen, Yunfei Ma and Xisheng Jia
Sensors 2022, 22(10), 3884; https://doi.org/10.3390/s22103884 - 20 May 2022
Cited by 4 | Viewed by 1594
Abstract
It is critical to deploy wireless data transmission technologies remotely, in real-time, to monitor the health state of diesel engines dynamically. The usual approach to data compression is to collect data first, then compress it; however, we cannot ensure the correctness and efficiency [...] Read more.
It is critical to deploy wireless data transmission technologies remotely, in real-time, to monitor the health state of diesel engines dynamically. The usual approach to data compression is to collect data first, then compress it; however, we cannot ensure the correctness and efficiency of the data. Based on sparse Bayesian optimization block learning, this research provides a method for compression reconstruction and fault diagnostics of diesel engine vibration data. This method’s essential contribution is combining compressive sensing technology with fault diagnosis. To achieve a better diagnosis effect, we can effectively improve the wireless transmission efficiency of the vibration signal. First, the dictionary is dynamically updated by learning the dictionary using singular value decomposition to produce the ideal sparse form. Second, a block sparse Bayesian learning boundary optimization approach is utilized to recover structured non-sparse signals rapidly. A detailed assessment index of the data compression effect is created. Finally, the experimental findings reveal that the approach provided in this study outperforms standard compression methods in terms of compression efficiency and accuracy and its ability to produce the desired fault diagnostic effect, proving the usefulness of the proposed method. Full article
(This article belongs to the Special Issue Machine Health Monitoring and Fault Diagnosis Techniques)
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16 pages, 4181 KiB  
Article
Performance Degradation Prediction Using LSTM with Optimized Parameters
by Yawei Hu, Ran Wei, Yang Yang, Xuanlin Li, Zhifu Huang, Yongbin Liu, Changbo He and Huitian Lu
Sensors 2022, 22(6), 2407; https://doi.org/10.3390/s22062407 - 21 Mar 2022
Cited by 8 | Viewed by 2692
Abstract
Predicting the degradation of mechanical components, such as rolling bearings is critical to the proper monitoring of the condition of mechanical equipment. A new method, based on a long short-term memory network (LSTM) algorithm, has been developed to improve the accuracy of degradation [...] Read more.
Predicting the degradation of mechanical components, such as rolling bearings is critical to the proper monitoring of the condition of mechanical equipment. A new method, based on a long short-term memory network (LSTM) algorithm, has been developed to improve the accuracy of degradation prediction. The model parameters are optimized via improved particle swarm optimization (IPSO). Regarding how this applies to the rolling bearings, firstly, multi-dimension feature parameters are extracted from the bearing’s vibration signals and fused into responsive features by using the kernel joint approximate diagonalization of eigen-matrices (KJADE) method. Then, the between-class and within-class scatter (SS) are calculated to develop performance degradation indicators. Since network model parameters influence the predictive accuracy of the LSTM model, an IPSO algorithm is used to obtain the optimal prediction model via the LSTM model parameters’ optimization. Finally, the LSTM model, with said optimal parameters, was used to predict the degradation trend of the bearing’s performance. The experiment’s results show that the proposed method can effectively identify the trends of degradation and performance. Moreover, the predictive accuracy of this proposed method is greater than that of the extreme learning machine (ELM) and support vector regression (SVR), which are the algorithms conventionally used in degradation modeling. Full article
(This article belongs to the Special Issue Machine Health Monitoring and Fault Diagnosis Techniques)
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