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Electronics
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Fault Detection, Diagnosis, and Prognosis Techniques towards System Reliability Enhancement
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Fault Detection, Diagnosis, and Prognosis Techniques towards System Reliability Enhancement

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: closed (15 February 2024) | Viewed by 12364

Special Issue Editors

Prof. Dr. Hamid Reza Karimi

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Guest Editor
Department of Mechanical Engineering, Politecnico di Milano, Via La Masa 1, 20156 Milan, Italy
Interests: control theory; mechatronics; sensors and actuators; motion control; vibration control; vehicle dynamics; fault detection; health monitoring; wind energy
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Len Gelman

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Guest Editor
School of Computing and Engineering, Department of Engineering and Technology, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK
Interests: digital signal processing; structural health monitoring; condition monitoring; artificial intelligence; vibration analysis; motor current signature analysis; adaptation of diagnosis systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Defeng Wu

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Guest Editor
School of Marine Engineering, Jimei University, Xiamen 361021, China
Interests: dynamic positioning system; unmanned surface vehicle; artificial intelligence application; control theory and application
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dongsheng Yang

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Guest Editor
College of Information Science and Engineering, Institute of Intelligent Electrical Science and Technology, Northeastern University, Shenyang 110819, China
Interests: automation theory and applications; control engineering; mechatronics; artificial intelligence; fault diagnosis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to enhance the safety and reliability of practical systems by providing a platform for sharing the latest developments in the fault detection, diagnosis, prognosis (FDDP), and reliability analysis of mechatronics systems. In particular, both traditional model-based and model-free techniques, with special attention paid to artificial intelligence and machine learning methods, may be employed for these purposes. Specifically, new solutions are required, particularly in the areas of system knowledge, data acquisition, signal processing, fault classification, fault prognosis, reliability analysis, and maintenance to outline the present status of system fault diagnosis and reliability in both theory and practice. New ideas should be incorporated in the future analysis and design of practical systems to tackle challenges facing the next generation of rotary machines and mechatronic systems.

This Special Issue will compile recent efforts contributing to fault detection, fault diagnosis, and fault prognosis techniques in the context of reliability enhancement and automation for mechatronic and rotary machinery systems. Contributions addressing state-of-the-art developments, algorithms, and methodologies, as well as perspectives on future developments and applications, are also welcomed. Manuscripts should contain both theoretical and practical/experimental-oriented results. The topics of interest include but not limited to:

  • Model-based fault detection and fault-tolerant control techniques;
  • Data-driven FDDP techniques;
  • System reliability modeling, analysis, and optimization;
  • Condition monitoring and maintenance of complex systems;
  • Advanced signal processing techniques for FDDP;
  • Machine learning techniques for FDDP;
  • Reliability and resilience control for mechatronics systems;
  • Failure analysis and prediction methods for mechatronics systems;
  • Application studies such as turbomachinery, manufacturing, vehicles, and robotics.

Prof. Dr. Hamid Reza Karimi
Prof. Dr. Len Gelman
Prof. Dr. Defeng Wu
Prof. Dr. Dongsheng Yang
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. Electronics 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 2400 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

  • fault detection
  • fault diagnosis
  • failure prognosis
  • reliability analysis
  • machine learning

Published Papers (8 papers)

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Research

15 pages, 7141 KiB  
Article
Diagnosis of Stator Winding and Permanent Magnet Faults of PMSM Drive Using Shallow Neural Networks
by Maciej Skowron, Teresa Orlowska-Kowalska and Czeslaw T. Kowalski
Electronics 2023, 12(5), 1068; https://doi.org/10.3390/electronics12051068 - 21 Feb 2023
Cited by 7 | Viewed by 1506
Abstract
This paper presents the application of shallow neural networks (SNNs): multi-layer perceptron (MLP) and self-organizing Kohonen maps (SOMs) to the early detection and classification of the stator and rotor faults in permanent magnet synchronous motors (PMSMs). The neural networks were trained based on [...] Read more.
This paper presents the application of shallow neural networks (SNNs): multi-layer perceptron (MLP) and self-organizing Kohonen maps (SOMs) to the early detection and classification of the stator and rotor faults in permanent magnet synchronous motors (PMSMs). The neural networks were trained based on the vector coming from measurements on a real object. The elements of the input vector of SNNs constituted the selected amplitudes of the diagnostic signal spectrum. The stator current and axial flux were used as diagnostic signals. The test object was a 2.5 kW PMSM motor supplied by a frequency converter operating in a closed-loop control structure. The experimental verification of the proposed diagnostic system was carried out for variable load conditions and values of the supply voltage frequency. The obtained results were compared with an approach based on a deep neural network (DNN). The research presented in the article confirm the possibility of detection and assessing the individual damage of stator winding and permanent magnets as well as the simultaneous faults of the PMSM stator and rotor using SNNs with simple signal preprocessing. Full article
(This article belongs to the Special Issue Fault Detection, Diagnosis, and Prognosis Techniques towards System Reliability Enhancement)
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20 pages, 4108 KiB  
Article
The Performance of Electronic Current Transformer Fault Diagnosis Model: Using an Improved Whale Optimization Algorithm and RBF Neural Network
by Pengju Yang, Taoyun Wang, Heng Yang, Chuipan Meng, Hao Zhang and Li Cheng
Electronics 2023, 12(4), 1066; https://doi.org/10.3390/electronics12041066 - 20 Feb 2023
Cited by 11 | Viewed by 1435
Abstract
With the widely application of electronic transformers in smart grids, transformer faults have become a pressing problem. However, reliable fault diagnosis of electronic current transformers (ECT) is still an open problem due to the complexity and diversity of fault types. In order to [...] Read more.
With the widely application of electronic transformers in smart grids, transformer faults have become a pressing problem. However, reliable fault diagnosis of electronic current transformers (ECT) is still an open problem due to the complexity and diversity of fault types. In order to solve this problem, this paper proposes an ECT fault diagnosis model based on radial basis function neural network (RBFNN) and optimizes the model parameters and the network size of RBFNN simultaneously via an improved whale optimization algorithm (WOA) to improve the classification accuracy and robustness of RBFNN. Since the classical WOA is easy to fall into a locally optimal performance, a hybrid multi-strategies WOA algorithm (CASAWOA) is proposed for further improvement in optimization performance. Firstly, we introduced the tent chaotic map strategy to improve the population diversity of WOA. Secondly, we introduced nonlinear convergence factor and adaptive inertia weight to enhance the exploitation ability of the WOA. Finally, on the premise of ensuring the convergence speed of the algorithm, we modified the simulated annealing mechanism in order to prevent premature convergence. The benchmark function tests show that the CASAWOA outperforms other state-of-the-art WOA algorithms in terms of convergence speed and exploration ability. Furthermore, to validate the performance of ECT fault diagnosis model based on CASAWOA-RBFNN, a comprehensive analysis of eight fault diagnosis methods is conducted based on the ECT fault samples collected from the detection circuit. The experimental results show that the CASAWOA-RBFNN achieves an accuracy of 97.77% in ECT fault diagnosis, which is 9.8% better than WOA-RBF and which shows promising engineering practicality. Full article
(This article belongs to the Special Issue Fault Detection, Diagnosis, and Prognosis Techniques towards System Reliability Enhancement)
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21 pages, 3878 KiB  
Article
A Prognosis Technique Based on Improved GWO-NMPC to Improve the Trajectory Tracking Control System Reliability of Unmanned Underwater Vehicles
by Wenyang Gan, Tianxing Xia and Zhenzhong Chu
Electronics 2023, 12(4), 921; https://doi.org/10.3390/electronics12040921 - 12 Feb 2023
Cited by 3 | Viewed by 1196
Abstract
The dynamics model of the unmanned underwater vehicle (UUV) system is highly nonlinear, multi-degree-of-freedom, strongly coupled, and time-varying. Its motion control has been a complex problem due to the unknown information about and the uncertainty of the working environment. To improve the performance [...] Read more.
The dynamics model of the unmanned underwater vehicle (UUV) system is highly nonlinear, multi-degree-of-freedom, strongly coupled, and time-varying. Its motion control has been a complex problem due to the unknown information about and the uncertainty of the working environment. To improve the performance and reliability of UUV trajectory tracking control, a trajectory tracking method based on nonlinear model predictive control is designed, and an improved gray wolf optimization (IGWO) is proposed for the optimization of nonlinear model predictive control. The convergence factor of IGWO is designed as a nonlinear attenuation function, and the memory function is added to the position update equation to enhance the effect of trajectory tracking control. Through the simulation in the ROS environment, the influence of the convergence factor on the convergence rate of trajectory tracking error and tracking control performance is obtained. By comparing the tracking effects of several groups of reference trajectories, it is shown that the proposed method is universally applicable and effective to the trajectory tracking control of UUV. Compared with traditional gray wolf optimization (GWO), SQP, and other optimization algorithms, the reliability of the proposed method for UUV trajectory tracking control is demonstrated. Full article
(This article belongs to the Special Issue Fault Detection, Diagnosis, and Prognosis Techniques towards System Reliability Enhancement)
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22 pages, 5549 KiB  
Article
Rolling Bearing Fault Feature Selection Method Based on a Clustering Hybrid Binary Cuckoo Search
by Lijun Sun, Yan Xin, Tianfei Chen and Binbin Feng
Electronics 2023, 12(2), 459; https://doi.org/10.3390/electronics12020459 - 16 Jan 2023
Viewed by 1276
Abstract
In order to solve the low accuracy in rolling bearing fault diagnosis caused by irrelevant and redundant features, a feature selection method based on a clustering hybrid binary cuckoo search is proposed. First, the measured motor signal is processed by Hilbert–Huang transform technology [...] Read more.
In order to solve the low accuracy in rolling bearing fault diagnosis caused by irrelevant and redundant features, a feature selection method based on a clustering hybrid binary cuckoo search is proposed. First, the measured motor signal is processed by Hilbert–Huang transform technology to extract fault features. Second, a clustering hybrid initialization technique is given for feature selection, combining the Louvain algorithm and the feature number. Third, a mutation strategy based on Levy flight is proposed, which effectively utilizes high-quality information to guide subsequent searches. In addition, a dynamic abandonment probability is proposed based on population sorting, which can effectively retain high-quality solutions and accelerate the convergence of the algorithm. Experimental results from nine UCI datasets show the effectiveness of the proposed improvement strategy. The open-source bearing dataset is used to compare the fault diagnosis accuracy of different algorithms. The experimental results show that the diagnostic error rate of this method is only 1.13%, which significantly improves classification accuracy and effectively realizes feature dimension reduction in fault datasets. Compared to similar methods, the proposed method has better comprehensive performance. Full article
(This article belongs to the Special Issue Fault Detection, Diagnosis, and Prognosis Techniques towards System Reliability Enhancement)
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36 pages, 16840 KiB  
Communication
Novel Nonlinear High Order Technologies for Damage Diagnosis of Complex Assets
by Tomasz Ciszewski, Len Gelman and Andrew Ball
Electronics 2022, 11(23), 3885; https://doi.org/10.3390/electronics11233885 - 24 Nov 2022
Cited by 3 | Viewed by 1157
Abstract
For the first time worldwide, innovative techniques, generic non-linear higher-order unnormalized cross-correlations of spectral moduli, for the diagnosis of complex assets, are proposed. The normalization of the proposed techniques is based on the absolute central moments, that have been proposed and widely investigated [...] Read more.
For the first time worldwide, innovative techniques, generic non-linear higher-order unnormalized cross-correlations of spectral moduli, for the diagnosis of complex assets, are proposed. The normalization of the proposed techniques is based on the absolute central moments, that have been proposed and widely investigated in mathematical works. The existing higher-order, cross-covariances of complex spectral components are not sufficiently effective. The novel technology is comprehensively experimentally validated for induction motor bearing diagnosis via motor current signals. Experimental results, provided by the proposed technique, confirmed high overall probabilities of correct diagnoses for bearings at early stages of damage development. The proposed diagnosis technology is compared with existing diagnosis technology, based on the triple cross-covariance of complex spectral components. Full article
(This article belongs to the Special Issue Fault Detection, Diagnosis, and Prognosis Techniques towards System Reliability Enhancement)
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19 pages, 6884 KiB  
Article
Fault Diagnosis Method for an Underwater Thruster, Based on Load Feature Extraction
by Wenyang Gan, Qishan Dong and Zhenzhong Chu
Electronics 2022, 11(22), 3714; https://doi.org/10.3390/electronics11223714 - 13 Nov 2022
Cited by 4 | Viewed by 1095
Abstract
Targeting the problem of fault diagnosis in magnetic coupling underwater thrusters, a fault pattern classification method based on load feature extraction is proposed in this paper. By analyzing the output load characteristics of thrusters under typical fault patterns, the load torque model of [...] Read more.
Targeting the problem of fault diagnosis in magnetic coupling underwater thrusters, a fault pattern classification method based on load feature extraction is proposed in this paper. By analyzing the output load characteristics of thrusters under typical fault patterns, the load torque model of the thrusters is established, and two characteristic parameters are constructed to describe the different fault patterns of thrusters. Then, a thruster load torque reconstruction method, based on the sliding mode observer (SMO), and the fault characteristic parameter identification method, based on the least square method (LSM), are proposed. According to the identified fault characteristic parameters, a thruster fault pattern classification method based on a support vector machine (SVM) is proposed. Finally, the feasibility and superiority of the proposed aspects are verified, through comparative simulation experiments. The results show that the diagnostic accuracy of this method is higher than 95% within 5 seconds of the thruster fault. The lowest diagnostic accuracy of thrusters with a single failure state is 96.75%, and the average diagnostic accuracy of thrusters with five fault states is 98.65%. Full article
(This article belongs to the Special Issue Fault Detection, Diagnosis, and Prognosis Techniques towards System Reliability Enhancement)
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12 pages, 2012 KiB  
Article
Rail Fastener Status Detection Based on MobileNet-YOLOv4
by Junpeng Fu, Xingjie Chen and Zhaomin Lv
Electronics 2022, 11(22), 3677; https://doi.org/10.3390/electronics11223677 - 10 Nov 2022
Cited by 5 | Viewed by 1477
Abstract
As an important part of track inspection, the detection of rail fasteners is of great significance to improve the safety of train operation. Additionally, rail fastener detection belongs to small-target detection. The YOLOv4 algorithm is relatively fast in detection and has some advantages [...] Read more.
As an important part of track inspection, the detection of rail fasteners is of great significance to improve the safety of train operation. Additionally, rail fastener detection belongs to small-target detection. The YOLOv4 algorithm is relatively fast in detection and has some advantages in small-target detection. Therefore, YOLOv4 is used for rail fastener status detection. However, YOLOv4 still suffers from the following two problems in rail fastener status detection. First, the features extracted by the original feature extraction network of YOLOv4 are relatively rough, which is not conducive to crack anomaly detection on rail fasteners. In addition, the traditional convolutional neural network has a larger number of parameters and calculations, which are difficult to run on the embedded system with low memory and processing power. To effectively solve those two problems, this paper proposes a rail fastener status detection algorithm based on MobileNet-YOLOv4 (M-YOLOv4). The edge features and texture features of rail fasteners are very important for rail fastener detection, and CSPDarknet53 cannot effectively extract the features of fasteners. The MobileNet is used to replace the CSPDarknet53 feature extraction network in the YOLOv4 algorithm, which can extract subtle features of rail fasteners and reduce the number of parameters and calculations of the algorithm. The experimental results show that the M-YOLOv4 algorithm has high detection accuracy and low resource consumption in rail fastener status detection. The false-alarm rate (FAR), missed-alarm rate (MAR), and error rate (ER) were 5.71%, 1.67%, and 4.24%, respectively, and the detection speed reached 59.8 fps. Compared with YOLOv4, the number of parameters and calculations were reduced by about 80.75% and 83.20%, respectively. Full article
(This article belongs to the Special Issue Fault Detection, Diagnosis, and Prognosis Techniques towards System Reliability Enhancement)
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23 pages, 4082 KiB  
Article
A Novel Blockchain Approach for Improving the Security and Reliability of Wireless Sensor Networks Using Jellyfish Search Optimizer
by Viyyapu Lokeshwari Vinya, Yarlagadda Anuradha, Hamid Reza Karimi, Parameshachari Bidare Divakarachari and Venkatramulu Sunkari
Electronics 2022, 11(21), 3449; https://doi.org/10.3390/electronics11213449 - 25 Oct 2022
Cited by 2 | Viewed by 1655
Abstract
For the past few years, centralized decision-making is being used for malicious node identification in wireless sensor networks (WSNs). Generally, WSN is the primary technology used to support operations, and security issues are becoming progressively worse. In order to detect malicious nodes in [...] Read more.
For the past few years, centralized decision-making is being used for malicious node identification in wireless sensor networks (WSNs). Generally, WSN is the primary technology used to support operations, and security issues are becoming progressively worse. In order to detect malicious nodes in WSN, a blockchain-routing- and trust-model-based jellyfish search optimizer (BCR-TM-JSO) is created. Additionally, it provides the complete trust-model architecture before creating the blockchain data structure that is used to identify malicious nodes. For further analysis, sensor nodes in a WSN collect environmental data and communicate them to the cluster heads (CHs). JSO is created to address this issue by replacing CHs with regular nodes based on the maximum remaining energy, degree, and closeness to base station. Moreover, the Rivest–Shamir–Adleman (RSA) mechanism provides an asymmetric key, which is exploited for securing data transmission. The simulation outcomes show that the proposed BCR-TM-JSO model is capable of identifying malicious nodes in WSNs. Furthermore, the proposed BCR-TM-JSO method outperformed the conventional blockchain-based secure routing and trust management (BSRTM) and distance degree residual-energy-based low-energy adaptive clustering hierarchy (DDR-LEACH), in terms of throughput (5.89 Mbps), residual energy (0.079 J), and packet-delivery ratio (89.29%). Full article
(This article belongs to the Special Issue Fault Detection, Diagnosis, and Prognosis Techniques towards System Reliability Enhancement)
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