基于Park—WPT和WOA—LSSVM的异步电动机故障诊断方法

恵阿丽; 鹿伟强; 荣相; 魏礼鹏; 陈雯雅

doi:10.13272/j.issn.1671-251x.2021070035

基于Park—WPT和WOA—LSSVM的异步电动机故障诊断方法

doi: 10.13272/j.issn.1671-251x.2021070035

恵阿丽¹,
鹿伟强^1, ,,
荣相^2,3,
魏礼鹏^2,3,
陈雯雅^2,3

1. 西安科技大学电气与控制工程学院, 陕西西安 710054;
2. 中煤科工集团常州研究院有限公司, 江苏常州 213015;
3. 天地(常州)自动化股份有限公司, 江苏常州 213015

基金项目:

天地科技股份有限公司科技创新创业资金专项资助项目(2020-2-TD-CXY003，2020-TD-QN002)。

详细信息

作者简介:
恵阿丽(1975-)，女，陕西蒲城人，副教授，博士，主要研究方向为电动机与电器设备故障诊断，E-mail:50083@qq.com。

通讯作者:
鹿伟强(1994-)，男，江苏徐州人，硕士研究生，主要研究方向为电气设备故障诊断，E-mail:luredeer@163.com。

中图分类号: TD614
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出版历程
- 收稿日期: 2021-07-13
- 修回日期: 2021-11-27
- 刊出日期: 2021-12-20

Research on fault diagnosis method of asynchronous motor based on Park-WPT and WOA-LSSVM

HUI Ali¹,
LU Weiqiang^{1
, ,},
RONG Xiang^2,3,
WEI Lipeng^2,3,
CHEN Wenya^2,3

1. College of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an 710054, China;
2. CCTEG Changzhou Research Institute, Changzhou 213015, China;
3. Tiandi(Changzhou) Automation Co., Ltd., Changzhou 213015, China

摘要

摘要: 针对现有电动机多故障诊断技术诊断精度较差、成本高等问题，基于三相定子电流信号对异步电动机转子断条、气隙偏心及其混合故障进行研究，提出了一种基于Park-WPT（Park矢量变换融合小波包变换）和WOA-LSSVM（鲸鱼优化的最小二乘支持向量机）的异步电动机故障诊断方法。通过Park矢量变换对采集到的三相电流信号进行预处理，根据椭圆轨迹的畸变率提取信号特征，作为第1类特征量；对Park矢量模平方谱进行WPT，求取其分解系数的能量值，作为第2类特征量；采用WOA的收缩包围猎物和螺旋更新猎物位置的机制优化LSSVM 中的正则化参数和核宽度，根据提取的2类特征信号建立以WOA-LSSVM为基础的故障诊断模型。实验结果表明，基于Park矢量变换或WPT的单一特征提取算法对混合故障的识别效果较差，故障特征识别率分别为73.75%和88.33%，将2类特征组合后，故障识别率提高到97.08%；WOA-LSSVM的寻优速度较快，故障诊断正确率较高，综合性能优于PSO（粒子群优化）算法、GWO（灰狼优化）算法和GA（遗传算法）优化的LSSVM。
- 异步电动机 /
- 多故障诊断 /
- Park矢量变换 /
- 小波包变换 /
- 鲸鱼优化算法 /
- 最小二乘支持向量机
Abstract: In order to solve the problems of poor precision and high cost of the existing motor multiple fault diagnosis technology, the rotor broken, air gap eccentricity and their mixed faults of asynchronous motor are studied based on three-phase stator current signals, and a fault diagnosis method of asynchronous motor based on Park-WPT (Park-wavelet packet transform) and WOA-LSVM (whale optimized algorithm-least squares support vector machine) is proposed. The collected three-phase current signals are preprocessed through Park vector transformation, the signal characteristics are extracted according to the distortion rate of the elliptical trajectory and the signal characteristics are taken as the first type characteristic quantity. The wavelet packet transformation is performed on the Park vector modulus square spectrum so as to obtain the energy value of its decomposition coefficient as the second type characteristic quantity. The mechanism of WOA's shrinkage surrounding prey and spiral updating prey position is used to optimize the regularization parameters and kernel width in LSSVM, and a fault diagnosis model based on WOA-LSSVM is established based on the extracted two types of characteristic signals. The experimental results show that the single characteristic extraction algorithm based on Park vector transform or wavelet packet transform has poor recognition effect on mixed faults, and the recognition rates of fault characteristics are 73.75% and 88.33% respectively. The recognition rate is improved to 97.08% by combining the two types of characteristics. WOA-LSSVM has a faster optimization speed and a higher fault diagnosis accuracy rate. Its overall performance is better than PSO (particle swarm optimization) algorithm, GWO (grey wolf optimization) algorithm and GA (genetic algorithm) optimized LSSVM.
- asynchronous motor /
- multiple fault diagnosis /
- Park vector transform /
- wavelet packet transform /
- whale optimization algorithm /
- least squares support vector machine

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参考文献(15)

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