Review of intelligent fault diagnosis for permanent magnet synchronous motors in electric vehicles

Xu, Xiaowei; Xue, Qiao; Zhang, Nan; Feng, Jingyi; Wang, Xiaoqing

doi:10.1177/1687814020944323

Cited by 26 publications

(20 citation statements)

References 105 publications

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“…In order to meet these requirements, nowadays, it is recommended to use intelligent diagnostic methods. An extensive review of AI-based fault diagnostic methods for PMSMs is presented in [46]. The authors discussed methods that use artificial knowledge technology such as neural networks, expert systems and fuzzy logic to realize complex motor fault detection and condition monitoring.…”

Section: Introductionmentioning

confidence: 99%

On-line Detection and Classification of PMSM Stator Winding Faults Based on Stator Current Symmetrical Components Analysis and the KNN Algorithm

Pietrzak

Wolkiewicz

2021

Electronics

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The significant advantages of permanent magnet synchronous motors, such as very good dynamic properties, high efficiency and power density, have led to their frequent use in many drive systems today. However, like other types of electric motors, they are exposed to various types of faults, including stator winding faults. Stator winding faults are mainly inter-turn short circuits and are among the most common faults in electric motors. In this paper, the possibility of using the spectral analysis of symmetrical current components to extract fault symptoms and the machine-learning-based K-Nearest Neighbors (KNN) algorithm for the detection and classification of the PMSM stator winding fault is presented. The impact of the key parameters of this classifier on the effectiveness of stator winding fault detection and classification is presented and discussed in detail, which has not been researched in the literature so far. The proposed solution was verified experimentally using a 2.5 kW PMSM, the construction of which was specially prepared for carrying out controlled inter-turn short circuits.

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Section: Introductionmentioning

confidence: 99%

On-line Detection and Classification of PMSM Stator Winding Faults Based on Stator Current Symmetrical Components Analysis and the KNN Algorithm

Pietrzak

Wolkiewicz

2021

Electronics

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“…Intelligent CM and FD is considered as a key factor of fault diagnosis development. This section summarizes the challenges and the future trends of CM and FD based on sounds and AE for IM [268][269][270][271] : AE data for CM and FD of machines still in infant stage. However, more contribution must be done to evaluate the performance of it.…”

Section: Challenges and Future Trendsmentioning

confidence: 99%

Section: Challenges and Future Trendsmentioning

confidence: 99%

Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study

AlShorman

Alkahatni

Masadeh

et al. 2021

Advances in Mechanical Engineering

125

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Nowadays, condition-based maintenance (CBM) and fault diagnosis (FD) of rotating machinery (RM) has a vital role in the modern industrial world. However, the remaining useful life (RUL) of machinery is crucial for continuous monitoring and timely maintenance. Moreover, reduced maintenance costs, enhanced safety, efficiency, reliability, and availability are the main important industrial issues to maintain valuable and high-cost machinery. Undoubtedly, induction motor (IM) is considered to be a pivotal component in industrial machines. Recently, acoustic emission (AE) becomes a very accurate and efficient method for fault, leaks and fatigue detection and monitoring techniques. Moreover, CM and FD based on the AE of IM have been growing over recent years. The proposed research study aims to review condition monitoring (CM) and fault diagnosis (FD) studies based on sound and AE for four types of faults: bearings, rotor, stator, and compound. The study also points out the advantages and limitations of using sound and AE analysis in CM and FD. Existing public datasets for AE based analysis for CM and FD of IM are also mentioned. Finally, challenges facing AE based CM and FD for RM, especially for IM, and possible future works are addressed in this study.

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“…The input and output of each layer can be seen as a separate network structure. Equations (5) and (6) are the encoding and decoding process of the SDAE layer 1:…”

Section: Stacked Denoising Autoencoder Structurementioning

confidence: 99%

“…The generation of coupling fault will cause irreversible damage to the performance of the motor itself, especially in the high-temperature working environment, which will seriously affect the normal operation of the motor and electric vehicle. Therefore, the fault diagnosis analysis of electric vehicle PMSM is of great significance to the development of electric vehicles and motors [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Permanent Magnet Synchronous Motor Based on Stacked Denoising Autoencoder

Feng

Liu

et al. 2021

Entropy

Self Cite

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As a complex field-circuit coupling system comprised of electric, magnetic and thermal machines, the permanent magnet synchronous motor of the electric vehicle has various operating conditions and complicated condition environment. There are various forms of failure, and the signs of failure are crossed or overlapped. Randomness, secondary, concurrency and communication characteristics make it difficult to diagnose faults. Meanwhile, the common intelligent diagnosis methods have low accuracy, poor generalization ability and difficulty in processing high-dimensional data. This paper proposes a method of fault feature extraction for motor based on the principle of stacked denoising autoencoder (SDAE) combined with the support vector machine (SVM) classifier. First, the motor signals collected from the experiment were processed, and the input data were randomly damaged by adding noise. Furthermore, according to the experimental results, the network structure of stacked denoising autoencoder was constructed, the optimal learning rate, noise reduction coefficient and the other network parameters were set. Finally, the trained network was used to verify the test samples. Compared with the traditional fault extraction method and single autoencoder method, this method has the advantages of better accuracy, strong generalization ability and easy-to-deal-with high-dimensional data features.

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Review of intelligent fault diagnosis for permanent magnet synchronous motors in electric vehicles

Cited by 26 publications

References 105 publications

On-line Detection and Classification of PMSM Stator Winding Faults Based on Stator Current Symmetrical Components Analysis and the KNN Algorithm

On-line Detection and Classification of PMSM Stator Winding Faults Based on Stator Current Symmetrical Components Analysis and the KNN Algorithm

Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study

Fault Diagnosis of Permanent Magnet Synchronous Motor Based on Stacked Denoising Autoencoder

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