基于参数优化VMD和模糊熵的自动机故障诊断方法  

Automaton Fault Diagnosis Method Based on VMD With Parameter Optimization and Fuzzy Entropy

作　　者：刘宝生 邓三鹏[2] 雷敬 LIU Baosheng;DENG Sanpeng;LEI Jing(College of Mechanical Engineering,Tianjin Transportation Technical College,Tianjin 300110,China;Department of Mechanical and Electrical Engineering,Tianjin University of Technology and Education,Tianjin 300222,China;Fifth Military Represent Office of Chongqing District,Chongqing 404000,China)

机构地区：[1]天津交通职业学院机械工程学院,天津300110 [2]天津职业技术师范大学电气信息工程学院,天津300222 [3]驻重庆地区第五军代室,重庆404000

出　　处：《机械设计与研究》2022年第2期93-96,113,共5页Machine Design And Research

基　　金：国家青年自然科学基金资助项目(61301040)。

摘　　要：为提高自动机故障诊断精度,提出了基于参数优化变分模态分解(VMD)和模糊熵的自动机故障诊断方法。首先,针对变分模态分解(VMD)方法参数确定主要依靠经验的问题,利用秃鹰搜索算法(BES)对其参数进行优化。其次,以参数优化后的VMD为自动机故障信号分解方法实现信号的自适应分解,而后计算各本征模态函数(IMF)的模糊熵,得到故障特征向量。最后,通过支持向量机对故障特征进行识别,得到诊断结果。自动机故障诊断实例验证了方法的有效性。In order to improve the fault diagnosis accuracy of automaton,a fault diagnosis method based on variational mode decomposition with parameter optimization and fuzzy entropy is proposed.Firstly,according to the fact that the parameter determination of VMD method mainly depends on experience,the parameters of VMD method are optimized by the bald eagle search(BES).Secondly,VMD with parameter optimized is used for adaptive decomposition of the fault signal,and the fuzzy entropy of each intrinsic mode function(IMF)is calculated to obtain the fault feature vectors.Finally,the fault features are identified by support vector machine and the diagnosis results are obtained.An example of automaton fault diagnosis shows the effectiveness of the method.

关 键 词：变分模态分解 秃鹰搜索算法 参数优化 自动机 故障诊断 

分 类 号：TH16[机械工程—机械制造及自动化]