Machine Learning-Based Stator Current Data-Driven PMSM Stator Winding Fault Diagnosis

Pietrzak, Przemysław; Wolkiewicz, Marcin

doi:10.3390/s22249668

Cited by 13 publications

(4 citation statements)

References 51 publications

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Section: Data-driven Fdd Methods For Electric Motor Drivementioning

confidence: 99%

“…Support vector machine (SVM) is a supervised learning algorithm that uses a small dataset for training and binary classification. It aims to classify data by finding the hyperplane to distinguish between two classes, as shown in Figure 12 [109]. Extreme learning machine (ELM), different from SVM, is useful for multi-classification purposes and takes advantage of higher training speed.…”

Section: Data-driven Fdd Methods For Electric Motor Drivementioning

confidence: 99%

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Fault Detection and Diagnosis of the Electric Motor Drive and Battery System of Electric Vehicles

2023

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Fault detection and diagnosis (FDD) is of utmost importance in ensuring the safety and reliability of electric vehicles (EVs). The EV’s power train and energy storage, namely the electric motor drive and battery system, are critical components that are susceptible to different types of faults. Failure to detect and address these faults in a timely manner can lead to EV malfunctions and potentially catastrophic accidents. In the realm of EV applications, Permanent Magnet Synchronous Motors (PMSMs) and lithium-ion battery packs have garnered significant attention. Consequently, fault detection methods for PMSMs and their drives, as well as for lithium-ion battery packs, have become a prominent area of research. An effective FDD approach must possess qualities such as accuracy, speed, sensitivity, and cost-effectiveness. Traditional FDD techniques include model-based and signal-based methods. However, data-driven approaches, including machine learning-based methods, have recently gained traction due to their promising capabilities in fault detection. This paper aims to provide a comprehensive overview of potential faults in EV motor drives and battery systems, while also reviewing the latest state-of-the-art research in EV fault detection. The information presented herein can serve as a valuable reference for future endeavors in this field.

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Section: Data-driven Fdd Methods For Electric Motor Drivementioning

confidence: 99%

Section: Data-driven Fdd Methods For Electric Motor Drivementioning

confidence: 99%

Fault Detection and Diagnosis of the Electric Motor Drive and Battery System of Electric Vehicles

2023

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“…However, with signal preprocessing stage used in the fault diagnosis process, classic ML algorithms such as k-nearest neighbors (KNN) may be sufficient. Nevertheless, they have been extensively studied in the past for their applicability to induction motors and PMSM electrical faults diagnosis [ 30 , 31 , 32 ], rather than to detect PMSM demagnetization.…”

Section: Introductionmentioning

confidence: 99%

Demagnetization Fault Diagnosis of Permanent Magnet Synchronous Motors Based on Stator Current Signal Processing and Machine Learning Algorithms

Pietrzak

Wolkiewicz

2023

Sensors

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Reliable fault diagnosis and condition monitoring are essential for permanent magnet synchronous motor (PMSM) drive systems with high-reliability requirements. PMSMs can be subject to various types of damage during operation. Magnetic damage is a unique fault of PMSM and concerns the permanent magnet (PM) of the rotor. PM damage may be mechanical in nature or be related to the phenomenon of demagnetization. This article presents a machine learning (ML) based demagnetization fault diagnosis method for PMSM drives. The time-frequency domain analysis based on short-time Fourier transform (STFT) is applied in the process of PM fault feature extraction from the stator phase current signal. Moreover, two ML-based models are verified and compared in the process of the automatic fault detection of demagnetization fault. These models are k-nearest neighbors (KNN) and multiLayer perceptron (MLP). The influence of the input vector elements, key parameters and structures of the models used on their effectiveness is extensively analyzed. The results of the experimental verification confirm the very high effectiveness of the proposed method.

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“…A combination of various diagnostic methods, such as vibroacoustic diagnostics in conjunction with thermal imaging, helps to acquire additional information (so-called redundancy). Therefore, in the case of electric motors, in addition to the vibroacoustic method presented in this article, such a comprehensive procedure can include multiple methods, such as thermal imaging and measurements of the phase current, voltage and axial flux [38,39].…”

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confidence: 99%

Assessment of Suitability for Long-Term Operation of a Bucket Elevator: A Case Study

Sokolski

2023

Energies

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Bucket elevators generally operate on a 24/7 basis, and for this reason, one of the main requirements is their high reliability. This reliability can be ensured, among other things, by assessing the technical condition of drive assemblies and working assemblies and taking appropriate measures. Carrying out diagnostic measurements enables periodical monitoring of those mechanisms. Vibroacoustic methods are usually employed in operating conditions to measure vibration velocity and acceleration at specific points, and are used as diagnostic signals. This paper presents the results of tests of the intensity of vibrations generated in the drive unit of a large industrial bucket elevator. The analysis of the results in the time domain and frequency domain served as the basis for evaluating the suitability of the drive, and thus the elevator, for long-term operation.

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Machine Learning-Based Stator Current Data-Driven PMSM Stator Winding Fault Diagnosis

Cited by 13 publications

References 51 publications

Fault Detection and Diagnosis of the Electric Motor Drive and Battery System of Electric Vehicles

Fault Detection and Diagnosis of the Electric Motor Drive and Battery System of Electric Vehicles

Demagnetization Fault Diagnosis of Permanent Magnet Synchronous Motors Based on Stator Current Signal Processing and Machine Learning Algorithms

Assessment of Suitability for Long-Term Operation of a Bucket Elevator: A Case Study

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