一种基于多点峭度谱和最大相关峭度解卷积的滚动轴承故障诊断方法
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摘要
针对最大相关峭度解卷积(MCKD)方法需要预知准确的滚动轴承故障特征周期的不足,提出一种多点峭度谱(Mkurt spectrum)和MCKD相结合的滚动轴承故障诊断方法。利用多点峭度谱对采样信号进行处理,通过比较不同周期下解卷积结果输出的信号的多点峭度谱,对预先估计的故障特征周期进行修正,再将优化得到的故障周期的精确取值输入到MCKD算法中,增强原信号中周期性故障冲击特征,并通过包络解调来诊断故障类型。通过对仿真信号、6205轴承外圈故障和铁路货车轮对轴承复合故障的试验信号的分析表明:即使在未知准确转速的条件下,该方法依然可以有效地实现滚动轴承的故障诊断,具有较高的工程应用价值。
Considering the shortcoming of the maximum correlated kurtosis deconvolution(MCKD) method of being necessary to foreknow the precise fault feature period of rolling element bearings, a new fault diagnosis method was proposed for rolling element bearings based on the MCKD combined with the multipoint kurtosis spectrum(Mkurt spectrum). First, sampled signals were processed by the multipoint kurtosis spectrum method, through comparing the multipoint kurtosis of output signals from the deconvolution with different periods to modify the anticipated fault feature period. Next, the optimized fault feature period was put into the MCKD algorithm to strengthen the cyclical fault impact characteristics in the original signals. Then, the fault type was identified through the envelope demodulation. The analysis on the simulation signals, outer ring fault signals of a 6205 bearing and multi-fault railway wagon wheel set bearing signals show that the method proposed can realize the fault diagnosis of rolling element bearings effectively, even on the condition without the knowledge of accurate speed, which is of a high value of engineering application.
Considering the shortcoming of the maximum correlated kurtosis deconvolution(MCKD) method of being necessary to foreknow the precise fault feature period of rolling element bearings, a new fault diagnosis method was proposed for rolling element bearings based on the MCKD combined with the multipoint kurtosis spectrum(Mkurt spectrum). First, sampled signals were processed by the multipoint kurtosis spectrum method, through comparing the multipoint kurtosis of output signals from the deconvolution with different periods to modify the anticipated fault feature period. Next, the optimized fault feature period was put into the MCKD algorithm to strengthen the cyclical fault impact characteristics in the original signals. Then, the fault type was identified through the envelope demodulation. The analysis on the simulation signals, outer ring fault signals of a 6205 bearing and multi-fault railway wagon wheel set bearing signals show that the method proposed can realize the fault diagnosis of rolling element bearings effectively, even on the condition without the knowledge of accurate speed, which is of a high value of engineering application.
引文
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[9] 钟先友,赵春华,陈宝家,等.基于MCKD和重分配小波尺度谱的旋转机械复合故障诊断研究[J].振动与冲击,2015,34(7):156-161.ZHONG Xianyou,ZHAO Chunhua,CHEN Baojia,et al.Rotating machinery fault diagnosis based on maximum correlation kurtosis deconvolution and reassigned wavelet scalogram[J].Journal of Vibration and Shock,2015,34(7):156-161.
[10] ZHOU Haitao,CHEN Jin,DONG Guangming.Application of maximum correlated kurtosis deconvolution on rolling element bearing fault diagnosis[M]// Engineering Asset Management-Systems,Professional Practices and Certification.Berlin:Springer,2015.
[11] MIAO Yonghao,ZHAO Ming,LIN Jing,et al.Application of an improved maximum correlated kurtosis deconvolution method for fault diagnosis of rolling element bearings[J].Mechanical Systems and Signal Processing,2017,92:173-195.
[12] MCDONALD G L,ZHAO Q.Multipoint optimal minimum entropy deconvolution and convolution fix:application to vibration fault detection[J].Mechanical Systems and Signal Processing,2017,82:461-477.
[13] 张晓涛,唐力伟,王平,等.最小周期相关熵解卷积结合窄带解调的轴承复合故障诊断研究[J].振动工程学报,2015,28(4):666-672.ZHANG Xiaotao,TANG Liwei,WANG Ping,et al.The research of bearing multi-fault diagnosis based on minimum period correlated entropy deconvolution and narrowband demodulation[J].Journal of Vibration Engineering,2015,28(4):666-672.
[2] WIGGINS R A.Minimum entropy deconvolution[J].Geoexploration,1978,9(16):21-35.
[3] 姚炽伟,樊新海,黄梦.基于MED的滚动轴承故障特征提取方法及应用[J].装甲兵工程学院学报,2013,27(3):35-38.YAO Zhiwei,FAN Xinhai,HUANG Meng.The rolling bearing fault feature extraction method based on MED and its application[J].Journal of Armored Force Engineering Institute,2013,27(3):35-38.
[4] ENDO H,RANDALL R B.Application of a minimum entropy deconvolution filter to enhance autoregressive model based gear tooth fault detection technique[J].Mechanical Systems and Signal Processing,2007,21(2):906-919.
[5] 张龙,胡俊锋,熊国良.基于MED和SK的滚动轴承循环冲击特征增强[J].振动、测试与诊断,2017,37(1):97-101.ZHANG Long,HU Junfeng,XIONG Guoliang.Cyclic shock enhancement by the combination of minimum entropy deconvolution and spectral kurtosis[J].Journal of Vibration,Measurement and Diagnosis,2017,37(1):97-101.
[6] 王宏超,陈进,董广明.基于最小熵解卷积与稀疏分解的滚动轴承微弱故障特征提取[J].机械工程学报,2013,49(1):88-94.WANG Hongchao,CHEN Jin,DONG Guangming.Fault diagnosis method for rolling bearing’s weak fault based on minimum entropy deconvolution and sparse decomposition[J].Journal of Mechanical Engineering,2013,49(1):88-94.
[7] MCDONALD G L,ZHAO Q,ZUO M J.Maximum correlated kurtosis deconvolution and application on gear tooth chip fault detection[J].Mechanical Systems and Signal Processing,2012,33:237-255.
[8] 武兵,贾锋,熊晓燕.基于最大相关峭度反褶积的轴承故障诊断方法[J].振动、测试与诊断,2014,34(3):570-575.WU Bing,JIA Feng,XIONG Xiaoyang.Bearing fault diagnosis based on maximum correlated kurtosis deconvolutin[J].Journal of Vibration,Measurement and Diagnosis,2014,34(3):570-575.
[9] 钟先友,赵春华,陈宝家,等.基于MCKD和重分配小波尺度谱的旋转机械复合故障诊断研究[J].振动与冲击,2015,34(7):156-161.ZHONG Xianyou,ZHAO Chunhua,CHEN Baojia,et al.Rotating machinery fault diagnosis based on maximum correlation kurtosis deconvolution and reassigned wavelet scalogram[J].Journal of Vibration and Shock,2015,34(7):156-161.
[10] ZHOU Haitao,CHEN Jin,DONG Guangming.Application of maximum correlated kurtosis deconvolution on rolling element bearing fault diagnosis[M]// Engineering Asset Management-Systems,Professional Practices and Certification.Berlin:Springer,2015.
[11] MIAO Yonghao,ZHAO Ming,LIN Jing,et al.Application of an improved maximum correlated kurtosis deconvolution method for fault diagnosis of rolling element bearings[J].Mechanical Systems and Signal Processing,2017,92:173-195.
[12] MCDONALD G L,ZHAO Q.Multipoint optimal minimum entropy deconvolution and convolution fix:application to vibration fault detection[J].Mechanical Systems and Signal Processing,2017,82:461-477.
[13] 张晓涛,唐力伟,王平,等.最小周期相关熵解卷积结合窄带解调的轴承复合故障诊断研究[J].振动工程学报,2015,28(4):666-672.ZHANG Xiaotao,TANG Liwei,WANG Ping,et al.The research of bearing multi-fault diagnosis based on minimum period correlated entropy deconvolution and narrowband demodulation[J].Journal of Vibration Engineering,2015,28(4):666-672.