Adaptive time-frequency filtering based fault characteristics extraction method for gears under variable rotational speed

PDF(1688 KB)

Journal of Vibration and Shock ›› 2018, Vol. 37 ›› Issue (10) : 135-140.

CHEN Xiangmin ZHANG Kang JING Fenghua LI Luping

Author information +

History +

Abstract

Aiming at the fault signal separation and fault characteristics extraction of gears under variable rotational speed, an adaptive time-frequency filtering method based on the chirplet path pursuit (CPP) and S transform was proposed. In the method, the gear mesh frequency was estimated from an original gear vibration signal by using the CPP, meanwhile, the timefrequency distribution of the original gear vibration signal was obtained by using the S transform. An adaptive time-frequency filter was designed according to the gear mesh frequency, and the time-frequency filtering was carried out on the time-frequency distribution of the original signal, which was followed by the inverse S transform so as to get the filtered signal containing gear fault informations. Then, the gear fault diagnosis was carried out according to the modulation sideband in the order spectrum, which was obtained by using order tracking to the filtered signal. The local faults of the gear were analysed both by simulations and examples. The results show that the adaptive timefrequency filter can adaptively change its centre frequency and bandwidth according to the frequency variation characteristics of the gear. It has a good adaptability for signal analysis, moreover，the filtered signal is without phase distortion. Therefore, the adaptive time-frequency filtering method is very suitable for analyzing non-stationary gear signals under variable rotational speed.

Key words

Adaptive time-frequency filtering / Chirplet path pursuit / S transform / Variable rotational speed；Gear

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

CHEN Xiangmin ZHANG Kang JING Fenghua LI Luping. Adaptive time-frequency filtering based fault characteristics extraction method for gears under variable rotational speed[J]. Journal of Vibration and Shock, 2018, 37(10): 135-140

References

[1] 丁康，李巍华，朱小勇. 齿轮及齿轮箱故障诊断实用技术[M]. 北京：机械工业出版社，2005.
DING Kang，LI Weihua，ZHU Xiaoyong. The gear and gearbox fault diagnosis practical technology[M]. Beijing：China Machine Press，2005.
[2] 王平，廖明夫. 滚动轴承故障诊断的自适应共振解调技术[J]. 航空动力学报，2005，20(4): 606-612.
WANG Ping, LIAO Mingfu. Adaptive demodulated resonance technique for the rolling bearing fault diagnosis[J]. Journal of Aerospace Power, 2005, 20(4): 606-612.
[3] JENA D P, SAHOO, S, PANIGRAHI S N. Gear fault diagnosis using active noise cancellation and adaptive wavelet transform[J]. Measurement, 2014, 47(1): 356-372.
[4] YAN Ruqiang, GAO R X, CHEN Xuefeng. Wavelet for fault diagnosis of rotary machines: A review with applications[J]. Signal Processing, 2014, 96(5): 1-15.
[5] CHENG Junsheng, YU Dejie, YANG Yu. The application of energy operator demodulation approach based on EMD in machinery fault diagnosis[J]. Mechanical Systems and Signal Processing, 2007, 21(2): 668-677
[6] LEI Yaguo, LIN Jing, HE Zhengjia, et al. A review on empirical mode decomposition in fault diagnosis of rotating machinery[J]. Mechanical System and Signal Processing, 2013, 35(1-2): 108-126.
[7] ChENG Junsheng, YANG Yu. A rotating machinery fault diagnosis method based on local mean decomposition[J]. Digital Signal Processing, 2012, 22(2): 356-366.
[8] 王衍学，何正嘉，訾艳阳，等. 基于LMD的时频分析方法及其机械故障诊断应用研究[J]. 振动与冲击，2012，31(9): 9-12.
WANG Yanxue, HE Zhengjia, ZI Yanyang, et al. Several key issues of local mean decomposition method used in mechanical fault diagnosis[J]. Journal of Vibration and Shock, 2012, 31(9): 9-12.
[9] 彭富强，于德介，刘坚. 一种基于多尺度线调频基的稀疏信号分解方法[J]. 振动工程学报，2010，23(3): 333-338.
PENG Fuqiang, YU Dejie, LIU Jian. Sparse signal decomposition method based on multi-scale chirplet[J]. Journal of Vibration and Engineering, 2010, 23(3): 333-338.
[10] 腾伟，安宏文，马志勇，等. 基于时频滤波的汽轮机半速涡动故障成分提取[J]. 振动与冲击，2015，34(3): 178-182.
TENG Wei, AN Hongwen, MA Zhiyong, et al. Semi-speed oil whirl fault component extraction in a stream turbine based on time-frequency filtering[J]. Journal of Vibration and Shock, 2015, 34(3): 178-182.
[11] STOCKWELL R G, MANSINHA L, LOWE R P. Localization of the complex spectrum: the S transform[J]. IEEE Transactions on Signal Processing, 1996, 44(4): 998-1001.
[12] 郭远晶，魏燕定，周晓军，等. S变换时频谱SVD降噪的冲击特征提取方法[J]. 振动工程学报，2014，27(4): 621-628.
GUO Yuanjing, WEI Yanding, ZHOU Xiaojun, et al. Impact feature extracting method based on S transform time-frequency spectrum denoised by SVD[J]. Journal of Vibration and Engineering, 2014, 27(4): 621-628.
[13] 张云强，张培林，李兵. 分数阶时频局部二值模式谱在齿轮故障诊断中的应用[J]. 振动与冲击，2015，34(11): 122-127.
ZHANG Yunqiang, ZHANG Peilin, LI Bing. Application of fractional order time-frequency local binary pattern spectra in gear fault diagnosis[J]. Journal of Vibration and Shock, 2015，34(11): 122-127.
[14] 刘建敏，刘远宏，江鹏程，等. 基于包络S变换时频图像提取齿轮故障特征[J]. 振动与冲击，2014，33(1): 165-169.
LIU Jianmin, LIU Yuanhong, JIANG Pengcheng, et al. Extraction of gear fault features based on the envelope and time-frequency image of S transformation[J]. Journal of Vibration and Shock, 2014, 33(1): 165-169.
[15] WU Chunyan, LIU Jian, PENG Fuqiang, et al. Gearbox fault diagnosis using adaptive zero phase time-varying filter based on multi-scale chirplet sparse signal decomposition[J]. Chinese Journal of Mechanical Engineering, 2013, 26(4): 831-838.
[16] CANDES E J, CHARLTON P R, HELGASON H. Detecting highly oscillatory signals by chirplet path pursuit[J]. Applied and Computational Harmonic Analysis, 2008, 24(1)：14-40．
[17] LUO Jiesi, YU Dejie, LIANG Ming. Gear fault detection under time-varying rotating speed via joint application of multiscale chirplet path pursuit and multiscale morphology analysis[J]. Structural Health Monitoring, 2012, 11(5)：526-537.
[18] PENG Fuqiang, YU Dejie, LUO Jiesi. Sparse signal decomposition method based on multi-scale chirplet and its application to the fault diagnosis of gearboxes[J]. Mechanical System and Signal Processing, 2011, 25(2): 549-557.