基于LCD和MCKD的轴承故障诊断
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摘要
针对振动信号故障特征频率微弱且难以提取的问题,提出基于局部特征尺度分解(local characteristic-scale decomposition,LCD)和最大相关峭度反卷积(maximum correlated kurtosis deconvolution,MCKD)相结合的故障诊断方式。通过对待测信号进行LCD分解,得到一系列的内禀尺度分量(intrinsic scale component,ISC),并根据相关系数,即峭度的筛选原则选择重构所需的真实分量。再利用MCKD对重构信号进行降噪处理,最后对降噪后的信号进行包络解调,提取故障特征信息。实验证明该方法在轴承故障诊断上具有一定的可行性。
With the weak fault characteristic frequency of vibration signal and difficulty in extraction,a fault diagnosis method based on the combination of local characteristic-scale decomposition(LCD)and maximum correlated kurtosis deconvolution(MCKD)is proposed.Through the LCD decomposition of the measured signal,a series of intrinsic scale components(ISC)are obtained,and the true components required for reconstruction are selected according to the screening principle of the correlation coefficient-kurtosis.Then the MCKD is used to reduce the noise of the reconstructed signal,and finally the fault characteristic information is extracted by envelope demodulation of the signal after noise reduction.Experiments show that this method is feasible in bearing fault diagnosis.
With the weak fault characteristic frequency of vibration signal and difficulty in extraction,a fault diagnosis method based on the combination of local characteristic-scale decomposition(LCD)and maximum correlated kurtosis deconvolution(MCKD)is proposed.Through the LCD decomposition of the measured signal,a series of intrinsic scale components(ISC)are obtained,and the true components required for reconstruction are selected according to the screening principle of the correlation coefficient-kurtosis.Then the MCKD is used to reduce the noise of the reconstructed signal,and finally the fault characteristic information is extracted by envelope demodulation of the signal after noise reduction.Experiments show that this method is feasible in bearing fault diagnosis.
引文
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[9]吴印华,徐琼燕,李俊峰.基于局部特征尺度分解排列熵和线性局部且空间排列的故障特征提取方法[J].机械设计与研究,2017,33(1):27-30,34.WU Yinhua,XU Qiongyan,LI Junfeng.Fault feature extraction method based on local characteristic-scale decomposition permutation entropy and liner local tangent space alignment[J].Machine Design&Research,2017,33(1):27-30,34.(in Chinese)
[10]郭谋发,游林旭,洪翠,等.基于LCD-Hilbert谱奇异值和多级支持向量机的配电网故障识别方法[J].高电压技术,2017,43(4):1239-1247.GUO Moufa,YOU Linxu,HONG Cui,et al.Identification method of distribution network faults based on Singular value of LCD-Hilbert spectrums and multilevel SVM[J].High Voltage Engineering,2017,43(4):1239-1247.(in Chinese)
[11]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)
[12]任学平,李攀,乔海懋.基于CEEMD与MCKD的滚动轴承早期故障诊断[J].轴承,2018(8):50-54.REN Xueping,LI Pan,QIAO Haimao.Early fault diagnosis for rolling bearings based on CEEMD and MCKD[J].Bearing,2018(8):50-54.(in Chinese)
[2]陈伟,陈锦雄,江永全,等.基于RS-LSTM的滚动轴承故障识别[J].中国科技论文,2018,13(10):1134-1141.CHEN Wei,CHEN Jinxiong,JIANG Yongquan,et al.Fault identification of rolling bearing based on RS-LSTM[J].China Sciencepaper,2018,13(10):1134-1141.(in Chinese)
[3]谢俐,杨乐.基于局部特征尺度分解和奇异值差分谱的滚动轴承故障诊断方法[J].机械设计与研究,2017,33(4):82-85.XIE Li,YANG Le.Fault diagnosis method of rolling bearing based on local characteristic-scale decomposition and singular value difference spectrum[J].Machine Design&Research,2017,33(4):82-85.(in Chinese)
[4]朱良明,崔伟成.基于局部特征尺度分解与基本尺度熵的轴承故障诊断[J].机械传动,2017,41(9):183-188.ZHU Liangming,CUI Weicheng.Fault diagnosis of bearing based on local characteristic-scale decomposition and base-scale entropy[J].Journal of Mechanical Transmission,2017,41(9):183-188.(in Chinese)
[5]程军圣,杨怡,杨宇.局部特征尺度分解方法及其在齿轮故障诊断中的应用[J].机械工程学报,2012,48(9):64-71.CHENG Junsheng,YANG Yi,YANG Yu.Local characteristic-scale decomposition method and its application to gear fault diagnosis[J].Journal of Mechanical Engineering,2012,48(9):64-71.(in Chinese)
[6]崔伟成,李伟,孟凡磊,等.奇异值分解降噪结合局部特征尺度分解的轴承故障诊断[J].机械传动,2016,40(5):128-133.CUI Weicheng,LI Wei,MENG Fanlei,et al.Bearing fault diagnosis based on singular value decomposition denoising and local characteristic-scale decomposition[J].Journal of Mechanical Transmission,2016,40(5):128-133.(in Chinese)
[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]唐道龙,李宏坤,王朝阁,等.基于参数优化MCKD的行星齿轮箱微弱故障诊断研究[J].机电工程,2018,35(8):779-785.TANG Daolong,LI Hongkun,WANG Chaoge,et al.Fault diagnosis of planet gear box based on parameter optimization’s MCKD[J].Journal of Mechanical&Electrical Engineering,2018,35(8):779-785.(in Chinese)
[9]吴印华,徐琼燕,李俊峰.基于局部特征尺度分解排列熵和线性局部且空间排列的故障特征提取方法[J].机械设计与研究,2017,33(1):27-30,34.WU Yinhua,XU Qiongyan,LI Junfeng.Fault feature extraction method based on local characteristic-scale decomposition permutation entropy and liner local tangent space alignment[J].Machine Design&Research,2017,33(1):27-30,34.(in Chinese)
[10]郭谋发,游林旭,洪翠,等.基于LCD-Hilbert谱奇异值和多级支持向量机的配电网故障识别方法[J].高电压技术,2017,43(4):1239-1247.GUO Moufa,YOU Linxu,HONG Cui,et al.Identification method of distribution network faults based on Singular value of LCD-Hilbert spectrums and multilevel SVM[J].High Voltage Engineering,2017,43(4):1239-1247.(in Chinese)
[11]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)
[12]任学平,李攀,乔海懋.基于CEEMD与MCKD的滚动轴承早期故障诊断[J].轴承,2018(8):50-54.REN Xueping,LI Pan,QIAO Haimao.Early fault diagnosis for rolling bearings based on CEEMD and MCKD[J].Bearing,2018(8):50-54.(in Chinese)