基于动态PSO-MCKD-HHT的滚动轴承故障诊断方法研究与应用  

Research and application of rolling bearing fault diagnosis method based on dynamic PSO-MCKD-EMD

作　　者：李东炎 李常贤[2] Li Dongyan;Li Changxian(College of Automation and Electrical Engineering,Dalian Jiaotong University,Dalian 116021,China;National and Local Joint Engineering Research Center for Rail Transit Equipment Design and Manufacturing Technology,Dalian Jiaotong University,Dalian 116021,China)

机构地区：[1]大连交通大学自动化与电气工程学院,大连116021 [2]大连交通大学轨道交通装备设计与制造技术国家地方联合工程研究中心,大连116021

出　　处：《电子测量技术》2021年第21期12-18,共7页Electronic Measurement Technology

摘　　要：滚动轴承作为牵引电机的重要部件之一,其故障诊断的准确性对保证牵引电机的正常运转具有重要的意义。为提高轴承故障诊断的准确性及有效性,选用最大相关峭度解卷积(MCKD)结合希尔伯特-黄变换(HHT)的方法进行诊断。针对MCKD算法受移位数(M),滤波器阶数(L)和冲击信号周期(T)特别依赖于经验的选择,选用动态粒子群算法对其进行优化,以降低噪声信号干扰,突出由故障激发的脉冲信号。再利用HHT算法得到信号包络谱,可以更好的识别不同故障类型。将VS与MATLAB相结合,可实现诊断算法应用到高级开发语言环境下。利用CWRU轴承数据集对算法进行验证,验证结果表明,该方法能够有效增强故障特征,得到轴承内圈故障频率为162 Hz,轴承内圈故障频率为108 Hz,可准确识别轴承的故障类型。Rolling bearing is one of the important parts of traction motor,and the accuracy of its fault diagnosis is of great significance to ensure the normal operation of traction motor.In order to improve the accuracy and effectiveness of bearing fault diagnosis,the method of maximum correlation kurtosis deconvolution(MCKD) combined with Hilbert-Huang transform(HHT) is used for diagnosis.In view of the selection of MCKD algorithm subject to shift number(M),filter order(L) and shock signal period(T),it is particularly dependent on the choice of experience.The dynamic particle swarm algorithm is selected to optimize it to reduce noise signal interference.Pulse signal triggered by fault.Then use the HHT algorithm to get the signal envelope spectrum,which can better identify different types of faults.Combining VS with MATLAB can realize the application of diagnostic algorithms to the high-level development language environment.The algorithm was verified using the CWRU bearing data set.The verification results show that the method can effectively enhance the fault characteristics.The fault frequency of the bearing inner ring is 162 Hz and the bearing inner ring fault frequency is 108 Hz,which can accurately identify the fault type of the bearing.

关 键 词：轴承诊断 最大相关峭度解卷积 动态粒子群算法 混合编译 

分 类 号：TH133.3[机械工程—机械制造及自动化] TP18[自动化与计算机技术—控制理论与控制工程]