基于KPCA与SSA-SVM的电机转子断条故障诊断  

Fault diagnosis of broken rotor bars in squirrel cage motor based on KPCA and SSA-SVM

作　　者：祝洪宇[1] 孙禹娇 孙文鹏 贾朱植 Zhu Hongyu;Sun Yujiao;Sun Wenpeng;Jia Zhuzhi(School of Electronic and information Engineering,University of Science and Technology Liaoning,Anshan 114051,China;School of Applied Technology,University of Science and Technology Liaoning,Anshan 114051,China)

机构地区：[1]辽宁科技大学电子与信息工程学院,鞍山114051 [2]辽宁科技大学应用技术学院,鞍山114051

出　　处：《国外电子测量技术》2024年第12期247-258,共12页Foreign Electronic Measurement Technology

基　　金：辽宁省教育厅基本科研项目(JYTMS20230946)资助。

摘　　要：采用电机定子电流信号特征分析诊断转子断条故障时,存在特征量信息不足严重影响诊断准确率的问题。为了解决这一问题,提出一种核主元成分分析(KPCA)与麻雀搜索算法(SSA)优化支持向量机(SSA-SVM)相结合的鼠笼电机转子断条故障诊断方法。首先从时域和频域2个维度提取适用于电流信号分析的45种特征量,通过KPCA对特征量进行特征提取和特征选择;然后采用SSA优化SVM参数,构建基于SSA-SVM的故障智能诊断模型;最后将筛选后的特征向量输入到SSA-SVM模型中进行分类识别。在3 kW电机实验平台上对所提出的方法进行实验验证。实验结果表明,所提出的方法对不同运行工况和不同严重程度的故障电机能够诊断出转子断条故障,SSA-SVM模型故障诊断准确率为99.67%,相比于其他诊断算法具有更高的诊断性能。When diagnosing broken rotor bar faults in squirrel-cage motors using motor current signature analyses(MCSA),diagnostic accuracy suffer from insufficient characteristic information.This paper proposes a broken rotor bar fault diagnosis method combining kernel principal component analysis(KPCA)and support vector machine(SVM)optimized by sparrow search algorithm(SSA).Firstly,45 characteristic quantities suitable for current signal analysis are extracted from time domain waveform and spectrum of the phase current.The KPCA is used for feature extraction and data reduction from original features.Then,SSA is used to optimize the parameters of SVM,and a fault intelligent diagnosis model based on SSA-SVM is constructed.Finally,the selected feature vectors are input into the SSA-SVM model for classification and recognition.The proposed method is experimentally verified on a 3 kW motor test platform.The experimental results show that the proposed method can diagnose rotor bar breakage faults in motors under different operating conditions and different severity levels.The fault diagnosis accuracy of the SSA-SVM model is 99.67%,which has higher diagnostic performance compared to other diagnostic algorithms.

关 键 词：转子断条 核主成分分析 支持向量机 麻雀搜索算法 故障诊断 

分 类 号：TM343[电气工程—电机] TN06[电子电信—物理电子学]