scholarly journals Eccentricity fault diagnosis in a permanent magnet synchronous motor under nonstationary speed conditions

2017 ◽  
Vol 25 ◽  
pp. 1881-1893 ◽  
Author(s):  
Mustafa EKER ◽  
Mehmet AKAR
Keyword(s):  
Fault Diagnosis ◽  
Permanent Magnet ◽  
Permanent Magnet Synchronous Motor ◽  
Synchronous Motor

scholarly journals Support-Vector Machine Approach for Robust Fault Diagnosis of Electric Vehicle Permanent Magnet Synchronous Motor

2020 ◽  
Vol 12 (1) ◽  
pp. 10
Author(s):  
Chunheng Zhao ◽  
Yi Li ◽  
Matthew Wessner ◽  
Chinmay Rathod ◽  
Pierluigi Pisu
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Permanent Magnet ◽  
High Performance ◽  
Permanent Magnet Synchronous Motor ◽  
Synchronous Motor ◽  
Industrial Applications ◽  
Operating Conditions ◽  
Support Vector ◽  
Svm Classifier

Permanent magnet synchronous motor (PMSM) is a leading technology for electric vehicles (EVs) and other high-performance industrial applications. These challenging applications demand robust fault diagnosis schemes, but conventional strategies based on models, system knowledge, and signal transformation have limitations that degrade the agility of diagnosing faults. These methods require extremely detailed design and consideration to remain robust against noise and disturbances in the actual application. Recent advancements in artificial intelligence and machine learning have proven to be promising next-generation solutions for fault diagnosis. In this paper, a support-vector machine (SVM) utilizing sparse representation is developed to perform sensor fault diagnosis of a PMSM. A simulation model of the pertinent PMSM drive system for automotive applications is used to generate a set of labelled training example sets that the SVM uses to determine margins between normal and faulty operating conditions. The PMSM model includes input as a torque reference profile and disturbance as a constant road grade, against both of which faults must be detectable. Even with limited training, the SVM classifier developed in this paper is capable of diagnosing faults with a high degree of accuracy, suggesting that such methods are feasible for the demanding fault diagnosis challenge in PMSM.

scholarly journals Fault Diagnosis of Permanent Magnet Synchronous Motor Inter Turn Short Circuit Based on Deep Reinforcement Learning

2021 ◽  
Vol 2137 (1) ◽  
pp. 012004
Author(s):  
Fangli Li ◽  
Yanbo Wang ◽  
Song Xu ◽  
Yuanjiang Li
Keyword(s):  
Reinforcement Learning ◽  
Fault Diagnosis ◽  
Permanent Magnet ◽  
Short Circuit ◽  
Permanent Magnet Synchronous Motor ◽  
Synchronous Motor ◽  
Data Set ◽  
Adversarial Network ◽  
Diagnosis Accuracy ◽  
Negative Sequence Current

Abstract With the increasing market share of Permanent Magnet Synchronous Motor(PMSM), the fault diagnosis and prediction technology for PMSM is becoming increasingly important. Firstly, in order to solve the problem of insufficient fault sample data consisting of negative sequence current, electromagnetic torque and other inter turn short circuit fault feature terms, the Conditional Generation Adversarial Network(CGAN) is used to expand the data set. Then, with sufficient data, Dueling_DQN algorithm of deep reinforcement learning is used to train and optimize the extended data set. Finally, the effectiveness of the algorithm in the field of PMSM fault diagnosis is verified by simulation training. The results show that the fault diagnosis accuracy of the algorithm can be reached 97.5%, while improved the convergence speed and saved the time cost of fault diagnosis.

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