融合能量熵编码和分类模型的牵引电机故障诊断  

Fault Diagnosis of Traction Motor Based on Fusion of Energy Entropy Coding and Classification Model

作　　者：张坤鹏 李昊 安春兰 杨辉 张志超 ZHANG Kunpeng;LI Hao;AN Chunlan;YANG Hui;ZHANG Zhichao(School of Electrical and Automation Engineering,East China Jiaotong University,Nanchang 330013,China;Key Laboratory of Advanced Control&Optimization of Jiangxi Province,Nanchang 330013,China;State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure,Nanchang 330013,China;China Railway Signal and Communication Corporation Group Limited,Beijing 100070,China;Locomotive&Car Research Institute,China Academy of Railway Sciences Corporation Limited,Beijing 100081,China)

机构地区：[1]华东交通大学电气与自动化工程学院,江西南昌330013 [2]江西省先进控制与优化重点实验室,江西南昌330013 [3]轨道交通基础设施性能监测与保障国家重点实验室,江西南昌330013 [4]北京全路通信信号研究设计院集团有限公司,北京100070 [5]中国铁道科学研究院集团有限公司机车车辆研究所,北京100081

出　　处：《铁道学报》2023年第9期64-73,共10页Journal of the China Railway Society

基　　金：国家自然科学基金(U2034211,62063007);江西省重点研发计划(20192BBEL50034);江西省自然科学基金(20224BAB212021,20232BAB202029);江西省教育厅项目(GJJ200610);江西省研究生创新专项资金(YC2022-s527);中国国家铁路集团有限公司科技研究开发计划(N2022J028)。

摘　　要：针对牵引电机故障特征不明显、识别定位困难等问题,提出一种融合能量熵编码与分类模型的故障特征量化诊断方法。结合故障机理特性,对故障严重程度进行建模,用微弱电流信号重构对故障敏感的电磁转矩信号,建立基于经验模态分解能量熵和故障属性知识编码的故障特征矩阵;为消除牵引电机故障样本少、非线性模式识别对精确诊断的影响,提出一种改进的灰狼优化算法(IGWO)对支持向量机分类SVM模型参数进行辨识,通过对多类故障准确识别率寻优实现对牵引电机状态预测。在高速列车牵引系统半实物仿真平台进行优化模型对比试验,通过对故障诊断指标分析可知,能量熵编码与IGWO-SVM融合方案可以很好地识别牵引电机故障。To address the difficulty in detecting and isolating the obscure symptoms of incipient faults in traction motors,a quantitative fault feature diagnosis method combining the energy entropy coding and the classification model was proposed.Based on the characteristics of fault mechanisms,the severity of faults was modelled,and the electromagnetic torque signal sensitive to the faults was reconstructed using weak current signals.A fault feature matrix was established based on empirical mode decomposition energy entropy coding and fault attribute knowledge encoding.To eliminate the impact of few fault samples and nonlinear pattern recognition on accurate diagnosis of traction motor faults,an improved grey wolf optimization(IGWO)algorithm was proposed to identify the parameters of the support vector machine classification model.The prediction of the status of traction motors was realized by optimizing the accurate recognition rate of multiple types of faults.An optimization model comparison experiment was conducted on the hardware-in-the-loop simulation platform of high-speed train traction system.The analysis of fault diagnosis indicators shows that the fusion scheme of energy entropy coding and IGWO-SVM can effectively identify traction motor faults.

关 键 词：高速列车牵引电机 电磁转矩能量熵编码 改进的灰狼优化算法 分类优化模型 多类故障准确识别率 

分 类 号：U264.1[机械工程—车辆工程]