基于Vold-Kalman广义解调的变转速轴承和齿轮复合故障诊断
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摘要
现有的以谱峭度为核心的滤波算法无法同时分离提取由轴承和齿轮故障激起的高频共振带。针对该问题,该研究舍弃以共振带提取为核心的诊断思路,直接提取、重置和定量表达时变非平稳的故障特征成分,提出了基于Vold-Kalman广义解调的变转速滚动轴承和齿轮复合故障诊断策略。该策略的核心是利用Vold-Kalman滤波从原始信号的包络中提取时变非平稳的轴承和齿轮故障特征成分;通过广义解调变换(GDT)将上述提取的时变非平稳故障特征成分进行平稳化重置;利用快速傅里叶变换(FFT)对上述重置的故障特征成分进行定量表达;通过频率谱中峰值与理论频率点的对比完成故障点定位。其中用于提取、重置和识别故障特征成分的频率函数、相位函数和频率点可由转频方程和机械结构参数计算。仿真和实测信号的分析结果表明所提算法无需共振带选取和角域重采样即可完成变转速轴承和齿轮复合故障特征的提取。另外,与传统带通滤波方法的对比进一步表明该算法去除无关项干扰、突出故障特征成分的优越性。
The spectral kurtosis based filtering algorithms cannot work well for distinguishing the differences between the two resonance bands excited by bearing and gear multi-fault. Hence, the traditional band-pass based methods were given up, and based on the extraction, reset and quantitative expressing of time-varying nonstationary fault characteristics, a new strategy was proposed for the bearing and gear multi-fault diagnosis under time-varying speeds based on the Vold-Kalman generalized demodulation. The essence of the proposed method is as follows: extract the time-varying nonstationary fault feature components from the envelope of raw signals using the Vold-Kalman filtering algorithm, reset the extracted filtering components by the generalized demodulation transform, quantitatively express these demodulated components using the FFT, and lastly determine the faults by comparing the theoretical frequency points and the peaks in spectrums. The fault characteristic frequency functions and their corresponding phase functions and frequency poisnts were calculated according to the rotational frequency equation of the target bearing and the mechanical structure parameters. The analysis results of some practical simulated and experimental multi-fault signals under time-varying rotating speeds validate that the proposed method is reliable to diagnose faulted bearings and gears without any frequency band location and angular resampling. The contrastive analysis results further verify its superiority in the interference terms elimination.
The spectral kurtosis based filtering algorithms cannot work well for distinguishing the differences between the two resonance bands excited by bearing and gear multi-fault. Hence, the traditional band-pass based methods were given up, and based on the extraction, reset and quantitative expressing of time-varying nonstationary fault characteristics, a new strategy was proposed for the bearing and gear multi-fault diagnosis under time-varying speeds based on the Vold-Kalman generalized demodulation. The essence of the proposed method is as follows: extract the time-varying nonstationary fault feature components from the envelope of raw signals using the Vold-Kalman filtering algorithm, reset the extracted filtering components by the generalized demodulation transform, quantitatively express these demodulated components using the FFT, and lastly determine the faults by comparing the theoretical frequency points and the peaks in spectrums. The fault characteristic frequency functions and their corresponding phase functions and frequency poisnts were calculated according to the rotational frequency equation of the target bearing and the mechanical structure parameters. The analysis results of some practical simulated and experimental multi-fault signals under time-varying rotating speeds validate that the proposed method is reliable to diagnose faulted bearings and gears without any frequency band location and angular resampling. The contrastive analysis results further verify its superiority in the interference terms elimination.
引文
[1]LI Bing,ZHANG Xining.A new strategy of instantaneous angular speed extraction and its application to multistage gearbox fault diagnosis[J].Journal of Sound and Vibration,2017,396:340-355.
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[3]WANG Wenyi.Early detection of gear tooth cracking using the resonance demodulation technique[J].Mechanical Systems and Signal Processing,2001,15(5):887-903.
[4]李蓉,于德介,陈向民,等.基于线调频小波路径追踪算法与EEMD的齿轮箱复合故障诊断方法[J].振动与冲击,2014,33(3):51-56.LI Rong,YU Dejie,CHENG Xiangmin,et al.A compound fault diagnosis method for gearboxs based on chhirplet path pursuit and EEMD[J].Journal of Vibration and Shock,2014,33(3):51-56.
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[9]LUO Jiesi,YU Dejie,LIANG Ming.Application of multiscale chirplet path pursuit and fractional Fourier transform for gear fault detection in speed up and speed down processes[J].Journal of Sound and Vibration,2012,331:4971-4986.
[10]ZHAO Dezun,LI Jianyong,CHENG Weidong,et al.Compound faults detection of rolling element bearing based on the generalized demodulation algorithm under time-varying rotational speed[J].Journal of Sound and Vibration,2016,378:109-123.
[11]VOLD H,LEURIDAN J.High resolution order tracking at extreme slew rates,using Kalman tracking filters[J].Shock&Vibration,1993,2(6):507-515.
[12]VOLD H,BLOUGH J,MAINS M.Multi axle order tracking with the Vold-Kalman tracking filter[J].Journal of Sound and Vibration,1997,13(5):30-34.
[13]邓蕾,傅炜娜,董绍江,等.无转速计的旋转机械VoldKalman阶比跟踪研究[J].振动与冲击,2011,30(3):5-9.DENG Lei,FU Weina,DONG Shaojiang,et al.Tacholess Vold-Kalman order tracking of rotating machinery[J].Journal of Vibration and Shock,2011,30(3):5-9.
[14]温广瑞,江铖,李杨,等.基于弗德卡曼阶比跟踪的转子起停车故障特征提取方法及应用[J].振动与冲击,2016,35(2):64-68.WEN Guangrui,JIANG Cheng,LI Yang,et al.Fault feature extraction from the vibration signals in rotor start-up or slowdown processes based on order tracking and holospectrum[J].Journal of Vibration and Shock,2016,35(2):64-68.
[15]OLHEDE S,WALDEN A T.A generalized demodulation approach to time-frequency projections for multicomponent signals[J].Proceedings of the Royal Society A,2005,461(2059):2159-2179.
[16]黄椒治,林慧斌,丁康.基于广义解调平滑能量分离算法的瞬时频率估计[J].振动工程学报,2014,27(2):281-288.HUANG Jiaozhi,LIN Huibin,DING Kang.Instantaneous frequency estimation based on generalized demodulation smoothed energy separation algorithm[J].Journal of Vibration Engineering,2014,27(2):281-288.
[17]FELDAUER C,HOLDRICH R.Realisation of a VoldKalman tracking filter-A least square problem[C]∥Proceedings of the COST G-6 Conference on Digital Audio Effects(DAFX-410800).Verona:COST G,2000.
[18]TUMA J.Setting the pass bandwidth in the Vold-Kalman order tracking filter[C]∥Twelfth International Congress on Sound and Vibration.Lisbon:ICSV,2005.
[2]RANDALL R B,ANTONI J.Rolling element bearing diagnostics-a tutorial[J].Mechanical Systems and Signal Processing,2011,25(2):485-520.
[3]WANG Wenyi.Early detection of gear tooth cracking using the resonance demodulation technique[J].Mechanical Systems and Signal Processing,2001,15(5):887-903.
[4]李蓉,于德介,陈向民,等.基于线调频小波路径追踪算法与EEMD的齿轮箱复合故障诊断方法[J].振动与冲击,2014,33(3):51-56.LI Rong,YU Dejie,CHENG Xiangmin,et al.A compound fault diagnosis method for gearboxs based on chhirplet path pursuit and EEMD[J].Journal of Vibration and Shock,2014,33(3):51-56.
[5]ANTONI J.Fast computation of the Kurtogram for the detection of transient faults[J].Mechanical Systems and Signal Processing,2007,21(1):108-124.
[6]WANG D,PETER W T,TSUI K L.An enhanced Kurtogram method for fault diagnosis of rolling element bearings[J].Mechanical Systems and Signal Processing,2013,35(1):176-199.
[7]COMBET F,GELMAN L.Optimal filtering of gear signals for early damage detection based on the spectral kurtosis[J].Mechanical Systems and Signal Processing,2009,23(3):652-668.
[8]WANG Tianyang,CHU Fulei,HAN Qinkai,et al.Compound faults detection in gearbox via meshing resonance and spectral kurtosis methods[J].Journal of Sound and Vibration,2017,392:367-381.
[9]LUO Jiesi,YU Dejie,LIANG Ming.Application of multiscale chirplet path pursuit and fractional Fourier transform for gear fault detection in speed up and speed down processes[J].Journal of Sound and Vibration,2012,331:4971-4986.
[10]ZHAO Dezun,LI Jianyong,CHENG Weidong,et al.Compound faults detection of rolling element bearing based on the generalized demodulation algorithm under time-varying rotational speed[J].Journal of Sound and Vibration,2016,378:109-123.
[11]VOLD H,LEURIDAN J.High resolution order tracking at extreme slew rates,using Kalman tracking filters[J].Shock&Vibration,1993,2(6):507-515.
[12]VOLD H,BLOUGH J,MAINS M.Multi axle order tracking with the Vold-Kalman tracking filter[J].Journal of Sound and Vibration,1997,13(5):30-34.
[13]邓蕾,傅炜娜,董绍江,等.无转速计的旋转机械VoldKalman阶比跟踪研究[J].振动与冲击,2011,30(3):5-9.DENG Lei,FU Weina,DONG Shaojiang,et al.Tacholess Vold-Kalman order tracking of rotating machinery[J].Journal of Vibration and Shock,2011,30(3):5-9.
[14]温广瑞,江铖,李杨,等.基于弗德卡曼阶比跟踪的转子起停车故障特征提取方法及应用[J].振动与冲击,2016,35(2):64-68.WEN Guangrui,JIANG Cheng,LI Yang,et al.Fault feature extraction from the vibration signals in rotor start-up or slowdown processes based on order tracking and holospectrum[J].Journal of Vibration and Shock,2016,35(2):64-68.
[15]OLHEDE S,WALDEN A T.A generalized demodulation approach to time-frequency projections for multicomponent signals[J].Proceedings of the Royal Society A,2005,461(2059):2159-2179.
[16]黄椒治,林慧斌,丁康.基于广义解调平滑能量分离算法的瞬时频率估计[J].振动工程学报,2014,27(2):281-288.HUANG Jiaozhi,LIN Huibin,DING Kang.Instantaneous frequency estimation based on generalized demodulation smoothed energy separation algorithm[J].Journal of Vibration Engineering,2014,27(2):281-288.
[17]FELDAUER C,HOLDRICH R.Realisation of a VoldKalman tracking filter-A least square problem[C]∥Proceedings of the COST G-6 Conference on Digital Audio Effects(DAFX-410800).Verona:COST G,2000.
[18]TUMA J.Setting the pass bandwidth in the Vold-Kalman order tracking filter[C]∥Twelfth International Congress on Sound and Vibration.Lisbon:ICSV,2005.