基于MCKD和teager能量算子的滚动轴承复合故障诊断
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摘要
滚动轴承是旋转机械的主要部件之一,复杂多变的工作环境导致其频繁出现故障,且大部分情况下多种故障复合.针对这一问题,提出一种基于改进最大相关峭度解卷积(MCKD)和teager能量算子混合的滚动轴承复合故障诊断方法.该方法通过粒子群优化算法(PSO)对不同类型故障下MCKD的影响参数(L和M)进行寻优,设置与故障类型相对应的解卷积周期,以相关峭度最大化进行MCKD算法迭代运算,优化滤波器系数,改进的MCKD算法减少了噪声的干扰.然后利用teager能量算子具有检测信号瞬态冲击的优势,对信号的teager能量进行频谱分析,实现复合故障诊断.最后利用西储大学轴承数据和轴承故障模拟实验台对该方法进行验证,结果表明该方法能从滚动轴承单一和复合故障中有效提取故障特征信息,准确识别出故障类型.
Rolling bearing is one of the main parts of rotating machinery,but the complex and changeable working environment causes frequent failure and many kinds of composite fault.In order to solve this problem,a composite fault diagnosis method of rolling bearing based on improved maximum correlated kurtosis deconvolution(MCKD)and teager energy operator is proposed.In this method,particle swarm optimization(PSO)is used to optimize the MCKD parameters(Land M)of different types of faults,set up the deconvolution period corresponding to the fault type,calculate the MCKD algorithm with the maximum correlation kurtosis,and improve the filter coefficients.The improved MCKD algorithm reduces the noise interference to a great extent,and then use the teager energy operator to detect the transient impact of the signal,and analyze the teager energy spectrum to realize the composite fault diagnosis.Finally,the method is validated by using the bearing data of Case Western Reserve University and the bearing fault simulator,and the results show that it can effectively extract fault feature information from single and composite fault of rolling bearing and identify the fault type accurately.
Rolling bearing is one of the main parts of rotating machinery,but the complex and changeable working environment causes frequent failure and many kinds of composite fault.In order to solve this problem,a composite fault diagnosis method of rolling bearing based on improved maximum correlated kurtosis deconvolution(MCKD)and teager energy operator is proposed.In this method,particle swarm optimization(PSO)is used to optimize the MCKD parameters(Land M)of different types of faults,set up the deconvolution period corresponding to the fault type,calculate the MCKD algorithm with the maximum correlation kurtosis,and improve the filter coefficients.The improved MCKD algorithm reduces the noise interference to a great extent,and then use the teager energy operator to detect the transient impact of the signal,and analyze the teager energy spectrum to realize the composite fault diagnosis.Finally,the method is validated by using the bearing data of Case Western Reserve University and the bearing fault simulator,and the results show that it can effectively extract fault feature information from single and composite fault of rolling bearing and identify the fault type accurately.
引文
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[13]王天金,冯志鹏,郝如江,等.基于Teager能量算子的滚动轴承故障诊断研究[J].振动与冲击,2012,31(2):1-5,85.WANG Tianjin,FENG Zhipeng,HAO Rujiang,et al.Research on rolling bearing fault diagnosis based on Teager energy operator[J].Journal of Vibration and Shock,2012,31(2):1-5,85.(in Chinese)
[14]程军圣,杨怡,杨宇.基于LMD的能量算子解调机械故障诊断方法[J].振动,测试与诊断,2012,32(6):915-919,1033.CHENG Junsheng,YANG Yi,YANG Yu.LMD based energy operator demodulation method for mechanical fault diagnosis[J]. Journal of Vibration,Measurement&Diagnosis,2012,32(6):915-919,1033.(in Chinese)
[15]HE Wangpeng,ZI Yanyang,CHEN Binqiang,et al. Automaticfault feature extraction of mechanical anomaly on induction motor bearing using ensemble super-wavelet transform[J].Mechanical Systems and Signal Processing,2015,54-55:457-480.
[2] RANDALL R B, ANTONI J.Rolling element bearing diagnostics-A tutorial[J]. Mechanical Systems and Signal Processing,2011,25(2):485-520.
[3] OU Lu,YU Dejie,YANG Hanjian.A new rolling bearing fault diagnosis method based on GFT impulse component extraction[J]. Mechanical Systems and Signal Processing,2016,81:162-182.
[4] WANG Yanxue, HE Zhengjia, ZI Yanyang.Enhancement of signal denoising and multiple fault signatures detecting in rotating machinery using dual-treecomplexwavelettransform[J].Mechanical Systems and Signal Processing,2010,24(1):119-137.
[5]马新娜,杨绍普.滚动轴承复合故障诊断的自适应方法研究[J].振动与冲击,2016,35(10):145-150.MA Xinna,YANG Shaopu.Adaptive compound fault diagnosis of rolling bearing[J].Journal of Vibration and Shock,2016,35(10):145-150.(in Chinese)
[6] JIANG Yu,ZHU Hua,LI Z.A new compound faults detection method for rolling bearings based on empiricalwavelettransformandchaotic oscillator[J].Chaos,Solitons and Fractals,2016,89(SI):8-19.
[7]李辉,郑海起,唐力伟.基于改进形态分量分析的齿轮箱轴承多故障诊断研究[J].振动与冲击,2012,31(12):135-140.LI Hui,ZHENG Haiqi,TANG Liwei.Bearing multi-faultdiagnosisbasedonimproved morphological component analysis[J].Journal of Vibration and Shock,2012,31(12):135-140.(in Chinese)
[8] DYER D,STEWART R M.Detection of rolling element bearing damage by statistical vibration analysis[J].Journal of Mechanical Design,1978,100(2):229-235.
[9] SAWALHI N,RANDALL R B,ENDO H.The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis[J].Mechanical Systems and Signal Processing,2007,21(6):2616-2633.
[10]陈海周,王家序,汤宝平,等.基于最小熵解卷积和Teager能量算子直升机滚动轴承复合故障诊断研究[J].振动与冲击,2017,36(9):45-50,73.CHEN Haizhou,WANG Jiaxu,TANG Baoping,et al.Research on compound fault diagnosis of helicopter rolling bearing based on minimum entropy deconvolution and Teager energy operator[J].Journal of Vibration and Shock,2017,36(9):45-50,73.(in Chinese)
[11]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(1):237-255.
[12]夏均忠,赵磊,白云川,等.基于MCKD和VMD的滚动轴承微弱故障特征提取[J].振动与冲击,2017,36(20):78-83.XIA Junzhong,ZHAO Lei,BAI Yunchuan,et al.Feature extraction for rolling element bearing weak fault based on MCKD and VMD[J].Journal of Vibration and Shock,2017,36(20):78-83.(in Chinese)
[13]王天金,冯志鹏,郝如江,等.基于Teager能量算子的滚动轴承故障诊断研究[J].振动与冲击,2012,31(2):1-5,85.WANG Tianjin,FENG Zhipeng,HAO Rujiang,et al.Research on rolling bearing fault diagnosis based on Teager energy operator[J].Journal of Vibration and Shock,2012,31(2):1-5,85.(in Chinese)
[14]程军圣,杨怡,杨宇.基于LMD的能量算子解调机械故障诊断方法[J].振动,测试与诊断,2012,32(6):915-919,1033.CHENG Junsheng,YANG Yi,YANG Yu.LMD based energy operator demodulation method for mechanical fault diagnosis[J]. Journal of Vibration,Measurement&Diagnosis,2012,32(6):915-919,1033.(in Chinese)
[15]HE Wangpeng,ZI Yanyang,CHEN Binqiang,et al. Automaticfault feature extraction of mechanical anomaly on induction motor bearing using ensemble super-wavelet transform[J].Mechanical Systems and Signal Processing,2015,54-55:457-480.