采用改进多尺度符号动力学熵的铁路机车轴承故障诊断  

Fault Diagnosis of Railway Locomotive Bearings Using Improved Multiscale Symbolic Dynamic Entropy

作　　者：张龙 刘皓阳[1,2] 肖乾 Zhang Long;Liu Haoyang;Xiao Qian(Key Laboratory of Conveyance and Equipment of Ministry of Education,East China Jiaotong University,Nanchang 330013,China;State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure,East China Jiaotong University,Nanchang 330013,China)

机构地区：[1]华东交通大学载运工具与装备教育部重点实验室,江西南昌330013 [2]华东交通大学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

出　　处：《华东交通大学学报》2023年第5期32-40,共9页Journal of East China Jiaotong University

基　　金：国家自然科学基金项目(51665013);江西省自然科学基金项目(20212BAB204007,20224ACB204017)。

摘　　要：针对铁路机车轴承在真实复杂环境下故障特征难以提取而导致故障诊断困难的问题,提出一种改进多尺度符号动力学熵(IMSDE)的铁路机车轴承故障诊断方法。首先,通过邻域滑移均值化的方式改进多尺度符号动力学熵,克服了传统粗粒化造成的熵值偏差缺陷;然后,利用IMSDE充分提取振动信号在不同尺度下的关键故障特征;最后,结合极限学习机(ELM)实现铁路轴承不同故障类型与程度的识别。在此基础上,分别进行了3组试验分析。结果表明,对人为构造的轴承故障和工程实际产生的轴承故障,该方法都具有精准的故障识别效果,对比其他4种方法故障识别率更高,验证了该方法具有一定的工程实际应用价值。Aiming at the problem that it is difficult to extract the fault features of railway locomotive bearings in a real complex environment,which leads to the difficulty of fault diagnosis,an improved multiscale symbolic dynamic entropy(IMSDE)fault diagnosis method is proposed.Firstly,the MSDE is improved by utilizing neighborhood slip averaging,which overcomes the defects of entropy deviation caused by traditional coarse-graining.Then,IMSDE is used to fully extract the key fault features of vibration signals at different scales.Finally,the identification of different fault types and degrees of railway bearings is achieved by combining with an extreme learning machine(ELM).On this basis,three separate sets of tests were analyzed.The results show that the method has an accurate fault identification effect for both artificially constructed bearing faults and bearing faults generated by engineering reality,and the fault identification rate is higher compared with the other four methods,which verifies that the method has a certain value of practical application in engineering.

关 键 词：机车轴承 故障诊断 特征提取 多尺度符号动力学熵 极限学习机 

分 类 号：U279[机械工程—车辆工程] TH133.33[交通运输工程—载运工具运用工程]