Rolling Element Bearing fault characteristic information is within the second order cyclic stationary signal. But it is susceptible to noise interference. The method of cyclic periodogram based on the short-time Fourier transform used on the cyclic stationary signal analysis can improve the recognition ability of cyclic failure. But it is not good for weak fault characteristic identification and its results affected by the size of the window function. The method of optimal wavelet scales cyclic spectrum is proposed for detection of rolling element bearing early faults. The continuous wavelet transform is carried on vibration signal processing to obtain the wavelet coefficients firstly. Then, the optimal scale is selected by correlated kurtosis values. And then, the wavelet coefficients in this scale range are analyzed by using cyclic spectra along the time axis. At last, the average value of the cyclic spectra under the optimal scales is calculated for feature extraction. Comparison with the result of cyclic periodogram, it can be concluded that the proposed method has good performance for rolling element incipient fault feature extraction.
YANG Rui
,
LI Hongkun
,
HE Changbo
,
WANG Fengtao
. Rolling Element Bearing Incipient Fault Feature Extraction Based on Optimal Wavelet Scales Cyclic Spectrum[J]. Journal of Mechanical Engineering, 2018
, 54(17)
: 208
-217
.
DOI: 10.3901/JME.2018.17.208
[1] LIHOVD E,JOHANNESSEN TI,STEINEBACH C,et al. Intelligent diagnosis and maintenance management[J]. Journal of Intelligent Manufacturing,1998,9(6):523-537.
[2] 冯辅周,司爱威,饶国强,等. 基于小波相关排列熵的轴承早期故障诊断技术[J]. 机械工程学报,2012,48(13):73-79. FENG Fuzhou,SI Aiwei,RAO Guoqiang,et al. Early fault diagnosis technology for bearing based on wavelet correlation permutation entropy[J]. Journal of Mechanical Engineering,2012,48(13):73-79.
[3] 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.
[4] ANTONI J,BONNARDOT F,RAAD A,et al. Cyclostationary modelling of rotating machine vibration signals[J]. Mechanical Systems and Signal Processing,2004,18(6):1285-1314.
[5] 周福昌,陈进,何俊,等. 循环平稳信号处理在机械设备故障诊断中的应用综述[J]. 振动与冲击,2006,25(5):148-152. ZHOU Fuchang,CHEN Jin,HE Jun,et al. The application review of cyclostationary signal processing in mechanical equipment fault diagnosis[J]. Journal of Vibration and Shock,2006,25(5):148-152.
[6] ANTON I,GLOSSIOTIS G. Cyclostationary analysis of rolling-element bearing vibration signals[J]. Journal of Sound and Vibration,2001,248(5):829-845.
[7] RANDALL R B,ANTONI J,CHOBSAARD S. The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals[J]. Mechanical Systems and Signal Processing,2001,15(5):945-962.
[8] 陈向民,于德介,罗洁思. 基于线调频小波路径追踪阶比循环平稳解调的齿轮故障诊断[J]. 机械工程学报,2012,48(3):95-101. CHEN Xiangmin,YU Dejie,LUO Jiesi. Gear tooth fault diagnosis by using order cyclostationary demodulating approach based on chirplet path pursuit[J]. Journal of Mechanical Engineering,2012,48(3):95-101.
[9] ANTONI J. Cyclostationarity by examples[J]. Mechanical Systems and Signal Processing,2009,23(4):987-1036.
[10] ANTONI J,HANSON D. Detection of surface ships from interception of cyclostationary signature with the cyclic modulation coherence[J]. IEEE Journal of Oceanic Engineering,2012,37(3):478-493.
[11] 徐金梧,徐科. 小波变换在滚动轴承故障诊断中的应用[J]. 机械工程学报,1997,33(4):50-55. XU Jinwu,XU Ke. Application of wavelet transform in fault diagnosis of rolling bearings[J]. Chinese Journal of Mechanical Engineering,1997,33(4):50-55.
[12] 段晨东,高强,徐先峰. 频率切片小波变换时频分析方法在发电机组故障诊断中的应用[J]. 中国电机工程学报,2013,33(32):96-103. DUAN Chendong,GAO Qiang,XU Xianfeng. Generator unit fault diagnosis using the frequency slice wavelet transform time-frequency analysis method[J]. Journal of Chinese Electrical Engineering Science,2013,33(32):96-103.
[13] LI H,XU F,LIU H,et al. Incipient fault information determination for rolling element bearing based on synchronous averaging reassigned wavelet scalogram[J]. Measurement,2015,65:1-10.
[14] 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.
[15] 唐贵基,王晓龙. 最大相关峭度解卷积结合稀疏编码收缩的齿轮微弱故障特征提取[J]. 振动工程学报,2015,28(3):478-486. TANG Guiji,WANG Xiaolong. Weak feature extraction of gear fault based on maximum correlated kurtosis deconvolution and sparse code shrinkage[J]. Journal of Vibration Engineering,2015,28(3):478-486.
[16] TANG G,WANG X,HE Y. Diagnosis of compound faults of rolling bearings through adaptive maximum correlated kurtosis deconvolution[J]. Journal of Mechanical Science and Technology,2016,30(1):43-54.
[17] ORGHESANI P. The envelope-based cyclic periodogram[J]. Mechanical Systems and Signal Processing,2014,58:245-270.
[18] YAN R,GAO R X. Impact of wavelet basis on vibration analysis for rolling bearing defect diagnosis[C]//IEEE Instrumentation & Measurement Technology Conference, May 10-12,2011,Binjiang,Hangzhou,China:IEEE IMTC,2011,78(11):1-4.
[19] QIU H,LEE J,LIN J,et al. Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics[J]. Journal of Sound and Vibration,2006,289(4-5):1066-1090.
