Fault Diagnosis of Permanent Magnet DC Motors Based on Multi-Segment Feature Extraction

Sensors

卷:21

Weihao Wang¹  Lixin Lu¹ 

[1] School of Mechatronic Engineering and Automation, Shanghai University, 99 Shangda Road, BaoShan District, Shanghai 200444, China;

关键词: fault diagnosis;    permanent magnet DC motor;    support vector machine;    classification and regression tree;    k-nearest neighbor;    feature extraction;   

DOI  :  10.3390/s21227505

来源: DOAJ

【 摘 要 】

For permanent magnet DC motors (PMDCMs), the amplitude of the current signals gradually decreases after the motor starts. Only using the signal features of current in a single segment is not conducive to fault diagnosis for PMDCMs. In this work, multi-segment feature extraction is presented for improving the effect of fault diagnosis of PMDCMs. Additionally, a support vector machine (SVM), a classification and regression tree (CART), and the k-nearest neighbor algorithm (k-NN) are utilized for the construction of fault diagnosis models. The time domain features extracted from several successive segments of current signals make up a feature vector, which is adopted for fault diagnosis of PMDCMs. Experimental results show that multi-segment features have a better diagnostic effect than single-segment features; the average accuracy of fault diagnosis improves by 19.88%. This paper lays the foundation of fault diagnosis for PMDCMs through multi-segment feature extraction and provides a novel method for feature extraction.

【 授权许可】

Unknown