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Entropy
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Signal Processing for Fault Detection and Diagnosis in Electric Machines...
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Signal Processing for Fault Detection and Diagnosis in Electric Machines and Energy Conversion Systems

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".

Deadline for manuscript submissions: 20 May 2024 | Viewed by 6122

Special Issue Editor

Dr. Epaminondas D. Mitronikas

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Patras, 26504 Patras, Greece
Interests: design, analysis and construction of power electronic converters for driving DC and AC machines; field-oriented control of electric motors; industrial drives; microprocessor control of electric motors; PWM techniques; fault diagnosis of electrical machines and drives; electric vehicle propulsion systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electrical machines and energy conversion systems in general have become increasingly important over the last few decades. With the aim to achieve sustainability, the electrification of a wide range of applications is advancing, including in the consumer and industry sector, road vehicles and marine vessels. Electric machines are used almost everywhere either as motors or as generators. Meanwhile, modern energy conversion systems rely on power electronics to provide good performance, efficiency, and power quality.

As electric energy conversion systems and electric drives become more sophisticated, the appearance of an unpredicted fault may result in abnormal operation or system shutdown, decreasing its reliability. Therefore, timely fault diagnosis has become a prerequisite component to achieve reliability or fault-tolerant operation. The main task of a fault diagnosis methodology is to provide a warning when a problem (a fault) is detected in a system, and even detect the source of this fault. This is mostly achieved via signal processing methods, which analyze the electrical and/or mechanical quantities of the system to detect and locate the fault. In this regard, information obtained using mechanical and/or electrical sensors has to be processed. In the final step, fault indication and classification are provided, either as a result of frequency or time–frequency analysis of the signals or using artificial intelligence and machine learning methodologies.

In this Special Issue, unpublished original papers and reviews focused on (but not restricted to) the following research areas will be considered for publication:

  • Signal processing techniques for condition monitoring, fault detection and diagnosis of electric machines and drives;
  • Fault detection and diagnosis of power electronic converters;
  • Fault detection and diagnosis of energy conversion systems;
  • Signal processing methods for fault detection;
  • Signal processing methods for fault-tolerant systems;
  • Artificial intelligence and machine learning methods for fault detection and diagnosis of electric machines and energy conversion systems.

Dr. Epaminondas D. Mitronikas
Guest Editor

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. Entropy is an international peer-reviewed open access monthly 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

  • energy conversion systems
  • electric machines
  • power electronic converters
  • signal processing
  • fault detection
  • fault diagnosis
  • fault-tolerant systems
  • machine learning and systems theory
  • artificial intelligence

Published Papers (5 papers)

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Research

23 pages, 2872 KiB  
Article
Few-Shot Fault Diagnosis Based on an Attention-Weighted Relation Network
by Li Xue, Aipeng Jiang, Xiaoqing Zheng, Yanying Qi, Lingyu He and Yan Wang
Entropy 2024, 26(1), 22; https://doi.org/10.3390/e26010022 - 24 Dec 2023
Cited by 1 | Viewed by 899
Abstract
As energy conversion systems continue to grow in complexity, pneumatic control valves may exhibit unexpected anomalies or trigger system shutdowns, leading to a decrease in system reliability. Consequently, the analysis of time-domain signals and the utilization of artificial intelligence, including deep learning methods, [...] Read more.
As energy conversion systems continue to grow in complexity, pneumatic control valves may exhibit unexpected anomalies or trigger system shutdowns, leading to a decrease in system reliability. Consequently, the analysis of time-domain signals and the utilization of artificial intelligence, including deep learning methods, have emerged as pivotal approaches for addressing these challenges. Although deep learning is widely used for pneumatic valve fault diagnosis, the success of most deep learning methods depends on a large amount of labeled training data, which is often difficult to obtain. To address this problem, a novel fault diagnosis method based on the attention-weighted relation network (AWRN) is proposed to achieve fault detection and classification with small sample data. In the proposed method, fault diagnosis is performed through the relation network in few-shot learning, and in order to enhance the representativeness of feature extraction, the attention-weighted mechanism is introduced into the relation network. Finally, in order to verify the effectiveness of the method, a DA valve fault dataset is constructed, and experimental validation is performed on this dataset and another benchmark PU rolling bearing fault dataset. The results show that the accuracy of the network on DA is 99.15%, and the average accuracy on PU is 98.37%. Compared with the state-of-the-art diagnosis methods, the proposed method achieves higher accuracy while significantly reducing the amount of training data. Full article
(This article belongs to the Special Issue Signal Processing for Fault Detection and Diagnosis in Electric Machines and Energy Conversion Systems)
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19 pages, 5579 KiB  
Article
Remaining Useful Life Prediction of Rolling Bearings Based on Multi-scale Permutation Entropy and ISSA-LSTM
by Hongju Wang, Xi Zhang, Mingming Ren, Tianhao Xu, Chengkai Lu and Zicheng Zhao
Entropy 2023, 25(11), 1477; https://doi.org/10.3390/e25111477 - 25 Oct 2023
Viewed by 982
Abstract
The performance of bearings plays a pivotal role in determining the dependability and security of rotating machinery. In intricate systems demanding exceptional reliability and safety, the ability to accurately forecast fault occurrences during operation holds profound significance. Such predictions serve as invaluable guides [...] Read more.
The performance of bearings plays a pivotal role in determining the dependability and security of rotating machinery. In intricate systems demanding exceptional reliability and safety, the ability to accurately forecast fault occurrences during operation holds profound significance. Such predictions serve as invaluable guides for crafting well-considered reliability strategies and executing maintenance practices aimed at enhancing reliability. In the real operational life of bearings, fault information often gets submerged within the noise. Furthermore, employing Long Short-Term Memory (LSTM) neural networks for time series prediction necessitates the configuration of appropriate parameters. Manual parameter selection is often a time-consuming process and demands substantial prior knowledge. In order to ensure the reliability of bearing operation, this article investigates the application of three advanced techniques—Maximum Correlation Kurtosis Deconvolution (MCKD), Multi-Scale Permutation Entropy (MPE), and Long Short-Term Memory (LSTM) recurrent neural networks—for the prediction of the remaining useful life (RUL) of rolling bearings. The improved sparrow search algorithm (ISSA) is employed for configuring parameters in the Long Short-Term Memory (LSTM) network. Each technique’s principles, methodologies, and applications are comprehensively reviewed, offering insights into their respective strengths and limitations. Case studies and experimental evaluations are presented to assess their performance in RUL prediction. Findings reveal that MCKD enhances fault signatures, MPE captures complexity, and LSTM excels in modeling temporal patterns. The root mean square error of the prediction results is 0.007. The fusion of these techniques offers a comprehensive approach to RUL prediction, leveraging their unique attributes for more accurate and reliable predictions. Full article
(This article belongs to the Special Issue Signal Processing for Fault Detection and Diagnosis in Electric Machines and Energy Conversion Systems)
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19 pages, 5707 KiB  
Article
Dynamic Semi-Supervised Federated Learning Fault Diagnosis Method Based on an Attention Mechanism
by Shun Liu, Funa Zhou, Shanjie Tang, Xiong Hu, Chaoge Wang and Tianzhen Wang
Entropy 2023, 25(10), 1470; https://doi.org/10.3390/e25101470 - 21 Oct 2023
Viewed by 1492
Abstract
In cases where a client suffers from completely unlabeled data, unsupervised learning has difficulty achieving an accurate fault diagnosis. Semi-supervised federated learning with the ability for interaction between a labeled client and an unlabeled client has been developed to overcome this difficulty. However, [...] Read more.
In cases where a client suffers from completely unlabeled data, unsupervised learning has difficulty achieving an accurate fault diagnosis. Semi-supervised federated learning with the ability for interaction between a labeled client and an unlabeled client has been developed to overcome this difficulty. However, the existing semi-supervised federated learning methods may lead to a negative transfer problem since they fail to filter out unreliable model information from the unlabeled client. Therefore, in this study, a dynamic semi-supervised federated learning fault diagnosis method with an attention mechanism (SSFL-ATT) is proposed to prevent the federation model from experiencing negative transfer. A federation strategy driven by an attention mechanism was designed to filter out the unreliable information hidden in the local model. SSFL-ATT can ensure the federation model’s performance as well as render the unlabeled client capable of fault classification. In cases where there is an unlabeled client, compared to the existing semi-supervised federated learning methods, SSFL-ATT can achieve increments of 9.06% and 12.53% in fault diagnosis accuracy when datasets provided by Case Western Reserve University and Shanghai Maritime University, respectively, are used for verification. Full article
(This article belongs to the Special Issue Signal Processing for Fault Detection and Diagnosis in Electric Machines and Energy Conversion Systems)
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18 pages, 5417 KiB  
Article
Degradation-Sensitive Health Indicator Construction for Precise Insulation Degradation Monitoring of Electromagnetic Coils
by Yue Sun, Kai Wang, Aidong Xu, Beiye Guan, Ruiqi Li, Bo Zhang and Xiufang Zhou
Entropy 2023, 25(9), 1354; https://doi.org/10.3390/e25091354 - 19 Sep 2023
Viewed by 860
Abstract
Electromagnetic coils are indispensable components for energy conversion and transformation in various systems across industries. However, electromagnetic coil insulation failure occurs frequently, which can lead to serious consequences. To facilitate predictive maintenance for industrial systems, it is essential to monitor insulation degradation prior [...] Read more.
Electromagnetic coils are indispensable components for energy conversion and transformation in various systems across industries. However, electromagnetic coil insulation failure occurs frequently, which can lead to serious consequences. To facilitate predictive maintenance for industrial systems, it is essential to monitor insulation degradation prior to the formation of turn-to-turn shorts. This paper experimentally investigates coil insulation degradation from both macro and micro perspectives. At the macro level, an evaluation index based on a weighted linear combination of trend, monotonicity and robustness is proposed to construct a degradation-sensitive health indicator (DSHI) based on high-frequency electrical response parameters for precise insulation degradation monitoring. While at the micro level, a coil finite element analysis and twisted pair accelerated degradation test are conducted to obtain the actual turn-to-turn insulation status. The correlation analysis between macroscopic and microscopic effects of insulation degradation is used to verify the proposed DSHI-based method. Further, it helps to determine the threshold of DSHI. This breakthrough opens new possibilities for predictive maintenance for industrial equipment that incorporates coils. Full article
(This article belongs to the Special Issue Signal Processing for Fault Detection and Diagnosis in Electric Machines and Energy Conversion Systems)
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18 pages, 7801 KiB  
Article
Corn Harvester Bearing Fault Diagnosis Based on ABC-VMD and Optimized EfficientNet
by Zhiyuan Liu, Wenlei Sun, Saike Chang, Kezhan Zhang, Yinjun Ba and Renben Jiang
Entropy 2023, 25(9), 1273; https://doi.org/10.3390/e25091273 - 29 Aug 2023
Cited by 1 | Viewed by 1075
Abstract
The extraction of the optimal mode of the bearing signal in the drive system of a corn harvester is a challenging task. In addition, the accuracy and robustness of the fault diagnosis model are low. Therefore, this paper proposes a fault diagnosis method [...] Read more.
The extraction of the optimal mode of the bearing signal in the drive system of a corn harvester is a challenging task. In addition, the accuracy and robustness of the fault diagnosis model are low. Therefore, this paper proposes a fault diagnosis method that uses the optimal mode component as the input feature. The vibration signal is first decomposed by variational mode decomposition (VMD) based on the optimal parameters searched by the artificial bee colony (ABC). Moreover, the key components are screened using an evaluation function that is a fusion of the arrangement entropy, the signal-to-noise ratio, and the power spectral density weighting. The Stockwell transform is then used to convert the filtered modal components into time–frequency images. Finally, the MBConv quantity and activation function of the EfficientNet network are optimized, and the time–frequency pictures are imported into the optimized network model for fault diagnosis. The comparative experiments show that the proposed method accurately extracts the optimal modal component and has a fault classification accuracy greater than 98%. Full article
(This article belongs to the Special Issue Signal Processing for Fault Detection and Diagnosis in Electric Machines and Energy Conversion Systems)
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