Lu Lixin, Wang Weihao
School of Mechatronic Engineering and Automation, Shanghai University, 99 Shangda Road, BaoShan District, Shanghai 200444, China.
Sensors (Basel). 2021 Nov 11;21(22):7505. doi: 10.3390/s21227505.
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 -nearest neighbor algorithm (-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.
对于永磁直流电机（PMDCM），电机启动后电流信号的幅值会逐渐减小。仅使用单个时间段内电流的信号特征不利于永磁直流电机的故障诊断。在这项工作中，提出了多段特征提取方法以提高永磁直流电机故障诊断的效果。此外，利用支持向量机（SVM）、分类回归树（CART）和k近邻算法（k-NN）构建故障诊断模型。从电流信号的几个连续段中提取的时域特征组成一个特征向量，用于永磁直流电机的故障诊断。实验结果表明，多段特征比单段特征具有更好的诊断效果；故障诊断的平均准确率提高了19.88%。本文通过多段特征提取为永磁直流电机的故障诊断奠定了基础，并提供了一种新颖的特征提取方法。
