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

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

Deadline for manuscript submissions: 28 February 2025 | Viewed by 11335

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Special Issue Editor

Dr. Epaminondas D. Mitronikas

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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
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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

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Published Papers (9 papers)

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Research

23 pages, 15197 KiB

Open AccessArticle

Current and Stray Flux Combined Analysis for Sparking Detection in DC Motors/Generators Using Shannon Entropy

by Jorge E. Salas-Robles, Vicente Biot-Monterde and Jose A. Antonino-Daviu

Entropy 2024, 26(9), 744; https://doi.org/10.3390/e26090744 - 30 Aug 2024

Viewed by 434

Abstract

Brushed DC motors and generators (DCMs) are extensively used in various industrial applications, including the automotive industry, where they are critical for electric vehicles (EVs) due to their high torque, power, and efficiency. Despite their advantages, DCMs are prone to premature failure due to sparking between brushes and commutators, which can lead to significant economic losses. This study proposes two approaches for determining the temporal and frequency evolution of Shannon entropy in armature current and stray flux signals. One approach indirectly achieves this through prior analysis using the Short-Time Fourier Transform (STFT), while the other applies the Stockwell Transform (S-Transform) directly. Experimental results show that increased sparking activity generates significant low-frequency harmonics, which are more pronounced compared to mid and high-frequency ranges, leading to a substantial rise in system entropy. This finding enables the introduction of fault-severity indicators or Key Performance Indicators (KPIs) that relate the current condition of commutation quality to a baseline established under healthy conditions. The proposed technique can be used as a predictive maintenance tool to detect and assess sparking phenomena in DCMs, providing early warnings of component failure and performance degradation, thereby enhancing the reliability and availability of these machines. 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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11 pages, 1492 KiB

Open AccessArticle

Characteristic Extraction and Assessment Methods for Transformers DC Bias Caused by Metro Stray Currents

by Aimin Wang, Sheng Lin, Guoxing Wu, Xiaopeng Li and Tao Wang

Entropy 2024, 26(7), 595; https://doi.org/10.3390/e26070595 - 11 Jul 2024

Viewed by 578

Abstract

Metro stray currents flowing into transformer-neutral points cause the high neutral DC and a transformer to operate in the DC bias state.Because neutral DC caused by stray current varies with time, the neutral DC value cannot be used as the only characteristic indicator to evaluate the DC bias risk level. Thus, unified characteristic extraction and assessment methods are proposed to evaluate the DC bias risk of a transformer caused by stray current, considering the signals of transformer-neutral DC and vibration. In the characteristic extraction method, the primary characteristics are obtained by comparing the magnitude and frequency distributions of transformer-neutral DC and vibration with and without metro stray current invasion. By analyzing the correlation coefficients, the final characteristics are obtained by clustering the primary characteristics with high correlation. Then, the magnitude and frequency characteristics are extracted and used as indicators to evaluate the DC bias risk. Moreover, to avoid the influence of manual experience on indicator weights, the entropy weight method (EWM) is used to establish the assessment model. Finally, the proposed methods are applied based on the neutral DC and vibration test data of a certain transformer. The results show that the characteristic indicators can be extracted, and the transformer DC bias risk can be evaluated by using the proposed methods. 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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15 pages, 10099 KiB

Open AccessArticle

Separation and Extraction of Compound-Fault Signal Based on Multi-Constraint Non-Negative Matrix Factorization

by Mengyang Wang, Wenbao Zhang, Mingzhen Shao and Guang Wang

Entropy 2024, 26(7), 583; https://doi.org/10.3390/e26070583 - 9 Jul 2024

Viewed by 494

Abstract

To solve the separation of multi-source signals and detect their features from a single channel, a signal separation method using multi-constraint non-negative matrix factorization (NMF) is proposed. In view of the existing NMF algorithm not performing well in the underdetermined blind source separation, the β-divergence constraints and determinant constraints are introduced in the NMF algorithm, which can enhance local feature information and reduce redundant components by constraining the objective function. In addition, the Sine-bell window function is selected as the processing method for short-time Fourier transform (STFT), and it can preserve the overall feature distribution of the original signal. The original vibration signal is first transformed into time–frequency domain with the STFT, which describes the local characteristic of the signal from the time–frequency distribution. Then, the multi-constraint NMF is applied to reduce the dimensionality of the data and separate feature components in the low dimensional space. Meanwhile, the parameter WK is constructed to filter the reconstructed signal that recombined with the feature component in the time domain. Ultimately, the separated signals will be subjected to envelope spectrum analysis to detect fault features. The simulated and experimental results indicate the effectiveness of the proposed approach, which can realize the separation of multi-source signals and their fault diagnosis of bearings. In addition, it is also confirmed that the proposed method, juxtaposed with the NMF algorithm of the traditional objective function, is more applicable for compound fault diagnosis of the rotating machinery. 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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17 pages, 6146 KiB

Open AccessArticle

Entropy-Aided Meshing-Order Modulation Analysis for Wind Turbine Planetary Gear Weak Fault Detection under Variable Rotational Speed

by Shaodan Zhi, Hengshan Wu, Haikuo Shen, Tianyang Wang and Hongfei Fu

Entropy 2024, 26(5), 409; https://doi.org/10.3390/e26050409 - 8 May 2024

Cited by 3 | Viewed by 1090

Abstract

As one of the most vital energy conversation systems, the safe operation of wind turbines is very important; however, weak fault and time-varying speed may challenge the conventional monitoring strategies. Thus, an entropy-aided meshing-order modulation method is proposed for detecting the optimal frequency band, which contains the weak fault-related information. Specifically, the variable rotational frequency trend is first identified and extracted based on the time–frequency representation of the raw signal by constructing a novel scaling-basis local reassigning chirplet transform (SLRCT). A new entropy-aided meshing-order modulation (EMOM) indicator is then constructed to locate the most sensitive modulation frequency area according to the extracted fine speed trend with the help of order tracking technique. Finally, the raw vibration signal is bandpass filtered via the corresponding optimal frequency band with the highest EMOM indicator. The order components resulting from the weak fault can be highlighted to accomplish weak fault detection. The effectiveness of the proposed EMOM analysis-based method has been tested using the experimental data of three different gear fault types of different fault levels from a planetary test rig. 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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23 pages, 2872 KiB

Open AccessArticle

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 1408

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, 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

Open AccessArticle

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

Cited by 2 | Viewed by 1380

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 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

Open AccessArticle

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

Cited by 1 | Viewed by 1943

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, 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

Open AccessArticle

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 1216

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 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

Open AccessArticle

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 6 | Viewed by 1499

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 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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