基于包络解调滤波的滚动轴承复合故障诊断
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摘要
针对在变转速工况下进行滚动轴承复合故障诊断时,由于故障间的相互作用使得多种故障特征混叠在一起,彼此干扰,造成误判﹑漏判等问题,以及时域滤波过程中共振频带参数难以获得这一问题,提出基于包络解调滤波的滚动轴承复合故障诊断方法。首先,使用多项式函数拟合轴承转速,并根据轴承各部分的故障特征频率系数和转速信息计算瞬时故障特征频率(IFCF)趋势线;其次,根据各部分IFCF趋势线的拟合函数,构造解调算法的相位函数,对原始信号经过Hilbert变换得到的包络信号解调;最后,以各部分IFCF拟合函数的初始值为中心频率构造窄带巴特沃斯带通滤波器,对解调信号进行滤波处理,并对滤波后的信号进行快速傅里叶变换(FFT)得到包络解调滤波信号的频谱。研究结果表明:该算法使轴承IFCF的能量集中在初始值的位置,使用巴特沃斯带通滤波器便可以在包络信号中提取解调后的故障信息。该方法既克服了转速变化引起的"频率模糊"现象,又避免了时域滤波中共振频带难以确定的问题。仿真算例和应用实例证明了该算法的有效性。
Considering that erroneous judgment and false negative occur in bearing multi-fault diagnosis because of interaction between different faults, and that it is difficult to obtain resonance band filtering parameters under the condition of variable speed and load, the method of rolling bearing multi-fault diagnosis based on envelope demodulation filter algorithm was proposed. Firstly, according to fault characteristic frequency coefficient of each part of the bearing and polynomial fitting function of rotating speed, instantaneous fault characteristic frequency(IFCF) trend was calculated. Secondly, phase functions were constructed based on fitting functions of the obtained IFCF trends, and the envelope signal obtained by Hilbert transform was demodulated. Finally, filtered signal was obtained from the narrow band Butterworth band-pass filter in which the initial value of IFCFs was used as centre frequency, and then frequency spectrum of demodulation filtering signal was obtained by fast Fourier transform(FFT). The results show that the algorithm makes energy of IFCFs concentrate in the position of initial value, and band-pass filter can be used to extract demodulated fault information in envelope signal. The proposed method not only overcomes the "frequency ambiguity" phenomenon caused by the change of speed, but also avoids the difficulty of determining resonance band in time domain filtering. The effectiveness of the proposed method is validated by simulation and experimental results.
Considering that erroneous judgment and false negative occur in bearing multi-fault diagnosis because of interaction between different faults, and that it is difficult to obtain resonance band filtering parameters under the condition of variable speed and load, the method of rolling bearing multi-fault diagnosis based on envelope demodulation filter algorithm was proposed. Firstly, according to fault characteristic frequency coefficient of each part of the bearing and polynomial fitting function of rotating speed, instantaneous fault characteristic frequency(IFCF) trend was calculated. Secondly, phase functions were constructed based on fitting functions of the obtained IFCF trends, and the envelope signal obtained by Hilbert transform was demodulated. Finally, filtered signal was obtained from the narrow band Butterworth band-pass filter in which the initial value of IFCFs was used as centre frequency, and then frequency spectrum of demodulation filtering signal was obtained by fast Fourier transform(FFT). The results show that the algorithm makes energy of IFCFs concentrate in the position of initial value, and band-pass filter can be used to extract demodulated fault information in envelope signal. The proposed method not only overcomes the "frequency ambiguity" phenomenon caused by the change of speed, but also avoids the difficulty of determining resonance band in time domain filtering. The effectiveness of the proposed method is validated by simulation and experimental results.
引文
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[11]SAAVEDRA P N,RODRIGUEZ C G.Accurate assessment of computed order tracking[J].Shock and Vibration,2006,13(1):13-21.
[12]CHENG Weidong,GAO R X,WANG Jinjiang.Envelope deformation in computed order tracking and error in order analysis[J].Mechanical Systems and Signal Processing,2014,48(1/2):92-102.
[13]WANG Tiangyang,HAN Qinkai,CHU Fulei,et al.A new SKRgram based demodulation technique for planet bearing fault detection[J].Journal of Sound and Vibration,2016,385(22):330-349.
[14]CAI Guanghong.The processing of rotor startup signals based on empirical mode decomposition[J].Mechanical Systems and Signal Processing,2006,20(1):225-235.
[15]WU Zhaohua,HUANG N E.Ensemble empirical mode decomposition:a noise assisted data analysis method[J].Advances in Adaptive Data Analysis,2009,1(1):1-41.
[16]YEH J R,SHIEH J S.Complementary ensemble empirical mode decomposition:A noise enhanced data analysis method[J].Advances in Adaptive Data Analysis,2010,2(2):135-156.
[17]CHENG Junsheng,ZHANG Kang,YANG Yu.An order tracking technique for the gear fault diagnosis using local mean decomposition method[J].Mechanism and Machine Theory,2012,55:67-76.
[18]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.
[19]ANTONI J,RANDALL R B.The spectral kurtosis:application to the vibratory surveillance and diagnostics of rolling machines[J].Mechanical Systems and Signal Processing,2006,20(2):308-331.
[2]ZHAO Dezun,LI Jianyong,CHENG Weidong.Feature extraction of faulty rolling element bearing under variable rotational speed and gear interferences conditions[J].Shock and Vibration,2015,2015(3):1-9.
[3]CHEN Jinglong,ZI Yanyang,HE Zhengjia,et al.Compound faults detection of rotating machinery using improved adaptive redundant lifting multiwavelet[J].Mechanical Systems and Signal Processing,2013,38(1):36-54.
[4]WANG Tianyang,LIANG M,LI Jianyong,et al.Rolling element bearing fault diagnosis via fault characteristic order(FCO)analysis[J].Mechanical Systems and Signal Processing,2014,45(1):139-153.
[5]王晓冬,何正嘉,訾艳阳.小波自适应构造方法及滚动轴承复合故障诊断研究[J].振动工程学报,2010,23(4):438-444.WANG Xiaodong,HE Zhengjia,ZI Yanyang.Adaptive construction of multiwavelet and research on composite fault diagnosis of rolling bearing[J].Journal of Vibration Engineering,2010,23(4):438-444.
[6]明安波,褚福磊,张炜.滚动轴承复合故障特征分离的小波-频谱自相关方法[J].机械工程学报,2013,49(3):80-87.MING Anbo,CHU Fulei,ZANG Wei.Compound fault features separation of rolling element bearing based on the wavelet decomposition and spectrum auto-correlation[J].Journal of Mechanical Engineering,2013,49(3):80-87.
[7]CHEN Guohua,QIE Longfei,ZHANG Aijun,et al.Improved CICA algorithm used for single channel compound fault diagnosis of rolling bearings[J].Chinese Journal of Mechanical Engineering,2016,29(1):204-211.
[8]周俊,伍星,迟毅林,等.盲解卷积和频域压缩感知在轴承复合故障声学诊断的应[J].机械工程学报,2016,52(3):63-70.ZHOU Jun,WU Xing,CHI Yilin,et al.Blind deconvolution and frequency domain compressive sensing application in bearing composite acoustic fault diagnosis[J].Journal of Mechanical Engineering,2016,52(3):63-70.
[9]BONNARDOT F,BADAOUI M E,RANDALL R B,et al.Use of the acceleration signal of a gearbox in order to perform angular resampling(with limited speed fluctuation)[J].Mechanical Systems and Signal Processing,2005,19(4):766-785.
[10]郭瑜,秦树人.无转速计旋转机械升降速振动信号零相位阶比跟踪滤波[J].机械工程学报,2004,40(3):50-54.GUO Yu,QIN Shuren.Tacholess order tracking filtering for run-up or coast down vibration signal of rotating machinery based on zero-phase distortion digital filtering[J].Chinese Journal of Mechanical Engineering,2004,40(3):50-54.
[11]SAAVEDRA P N,RODRIGUEZ C G.Accurate assessment of computed order tracking[J].Shock and Vibration,2006,13(1):13-21.
[12]CHENG Weidong,GAO R X,WANG Jinjiang.Envelope deformation in computed order tracking and error in order analysis[J].Mechanical Systems and Signal Processing,2014,48(1/2):92-102.
[13]WANG Tiangyang,HAN Qinkai,CHU Fulei,et al.A new SKRgram based demodulation technique for planet bearing fault detection[J].Journal of Sound and Vibration,2016,385(22):330-349.
[14]CAI Guanghong.The processing of rotor startup signals based on empirical mode decomposition[J].Mechanical Systems and Signal Processing,2006,20(1):225-235.
[15]WU Zhaohua,HUANG N E.Ensemble empirical mode decomposition:a noise assisted data analysis method[J].Advances in Adaptive Data Analysis,2009,1(1):1-41.
[16]YEH J R,SHIEH J S.Complementary ensemble empirical mode decomposition:A noise enhanced data analysis method[J].Advances in Adaptive Data Analysis,2010,2(2):135-156.
[17]CHENG Junsheng,ZHANG Kang,YANG Yu.An order tracking technique for the gear fault diagnosis using local mean decomposition method[J].Mechanism and Machine Theory,2012,55:67-76.
[18]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.
[19]ANTONI J,RANDALL R B.The spectral kurtosis:application to the vibratory surveillance and diagnostics of rolling machines[J].Mechanical Systems and Signal Processing,2006,20(2):308-331.