An Online Metric Learning-Based Open-Switch and Current Sensor Fault Diagnosis for MSPMSM Systems

Du, Yutao; Li, Chi; Zheng, Zedong

doi:10.1109/tpel.2023.3265936

Cited by 10 publications

(1 citation statement)

References 32 publications

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“…In [134], open-switch and current sensor fault detection for a multi-sector PMSM was investigated based on a metric learning method. This technique takes advantage of a low computational burden, reduces the fault detection time to 1/10 of the current fundamental cycle and can detect and locate 28 combinations of open-switch fault and current sensor fault.…”

Section: Recent Data-driven Fdd Methods For Different Pmsm Motor Driv...mentioning

confidence: 99%

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: Recent Data-driven Fdd Methods For Different Pmsm Motor Driv...mentioning

confidence: 99%

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

2023

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An Improved Current Sensor Fault Diagnosis and Fault-tolerant Control for Multisector Permanent Magnet Synchronous Motor

Liu,

Zheng,

2024

2024 IEEE 7th International Electrical and Energy Conference (CIEEC)

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A Method of Faulted Section Location in Distribution Network Based on Siamese Network Under Small Sample

Wang,

Shao,

Shang

et al. 2023

2023 3rd International Conference on Energy, Power and Electrical Engineering (EPEE)

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An Online Metric Learning-Based Open-Switch and Current Sensor Fault Diagnosis for MSPMSM Systems

Cited by 10 publications

References 32 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

An Improved Current Sensor Fault Diagnosis and Fault-tolerant Control for Multisector Permanent Magnet Synchronous Motor

A Method of Faulted Section Location in Distribution Network Based on Siamese Network Under Small Sample

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