Multi-fault feature detection of rolling element bearing by iterative generalized demodulation algorithm under time-varying rotational speed

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Journal of Vibration and Shock ›› 2018, Vol. 37 ›› Issue (4) : 177-183.

ZHAO Dezun1 LI Jianyong1.2 CHENG Weidong1 WEN Weigang1

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Abstract

Due to the variable rotational speed, it is more difficult to extract multi-fault features of rolling element bearing, which may tightly couple together and interfere with each other. As such, a new multi-fault feature extraction method based on the iterative generalized demodulation algorithm is proposed. We utilize the time-frequency characteristics of the compound faults bearing signal and the merits of the generalized demodulation algorithm which can transform an interested curved time-frequency component of the non-stationary signal into linear path paralleling to the time axis to directly extract the multi-fault features. The proposed method can be summarized as four parts: obtain the equation of rotational frequency curve by rotational speed pulse signal, the phase functions and phase points are calculated based on the fault characteristic coefficient and the rotational frequency equation; calculate the envelope signal by the Hilbert transform; demodulate the envelope signal by the iterative generalized demodulation algorithm; obtain the frequency spectrum of demodulated signal to detect fault feature. The studies of simulated and experimental multi-fault bearing signals validate that the proposed method is reliable to diagnose multi-fault bearing under time-varying rotational speed.

Key words

rolling element bearing / multi-fault feature extraction / time-varying rotational speed / iterative generalized demodulation algorithm

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ZHAO Dezun1 LI Jianyong1.2 CHENG Weidong1 WEN Weigang1. Multi-fault feature detection of rolling element bearing by iterative generalized demodulation algorithm under time-varying rotational speed[J]. Journal of Vibration and Shock, 2018, 37(4): 177-183

References

[1] Azevedo HDMD, Araújo AM, Bouchonneau N. A review of wind turbine bearing condition monitoring: State of the art and challenges [J]. Renewable and Sustainable Energy Reviews, 2016, 56: 368-379.
[2] 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 Xiao-dong, HE Zheng-jia, ZI Yan-yang. Adaptive construction of multiwavelet and research on composite fault diagnosis of rolling bearing[J]. Journal of Vibration Engineering, 2010, 23(4): 438- 444.
[3] Luo Jie-si, Yu De-jie, Liang Ming. Application of multi-scale 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.
[4] 赵德尊, 李建勇, 程卫东. 变转速及齿轮噪源干扰下基于IDMM与EMD的滚动轴承故障诊断方法[J]. 振动与冲击, 2016, 35(10):101-119.
ZHAO De-zun, LI Jian-yong, CHENG Wei-dong. Method for rolling element bearing fault diagnosis based on IDMM and EMD under time-varying rotational speed and gear noise [J]. Journal of Vibration and Shock, 2016, 35(10):101-119.
[5] 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 An-bo, CHU Fu-lei, ZHANG 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.
[6] 马新娜, 杨绍普. 滚动轴承复合故障诊断的自适应方法研究[J]. 振动与冲击, 2016, 35(10):145-150.
MA Xin-na, YANG Shao-pu. Adaptive compound fault diagnosis of rolling bearings [J]. Journal of Vibration and Shock, 2016, 35(10):145-150.
[7] Chen Jing-long, Zi Yan-yang, He Zheng-jia, et al. Compound faults detection of rotating machinery using improved adaptive redundant lifting multiwavelet [J]. Mechanical Systems and Signal Processing, 2013, 38:36-54.
[8] Zhang Hai-bin, Lu Si-liang, He Qing-bo, et al. Multi-bearing defect detection with trackside acoustic signal based on a pseudo time–frequency analysis and Dopplerlet filter [J]. Mechanical Systems and Signal Processing, 2016, 70-71: 176-200.
[9] Fyfe K R, Munck E D S. Analysis of computed order tracking [J]. Mechanical System and Signal Processing. 1997, 11(2): 187-205.
[10] Randall R B, Antoni J. Rolling element bearing diagnostics-a tutorial [J]. Mechanical Systems and Signal Processing, 2011, 25 (2): 485-520.
[11] Saavedra P N, Rodriguez C G. Accurate assessment of computed order tracking [J]. Shock and Vibration, 2006, 13 (1):13-21.
[12] Cheng Wei-dong, Robert X G, Wang Jing-jiang, et al. Envelope deformation in computed order tracking and error in order analysis [J]. Mechanical System and Signal Processing, 2014, 48(1-2): 92-102.
[13] Olhehe 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.
[14] Cheng Jun-sheng, Yang Yu, YU De-jie. Application of the improved generalized demodulation time-frequency analysis method to multi-component signal decomposition [J]. Signal Processing, 2009, 89: 1205-1215.
[15] 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 Jiao-zhi, LIN Hui-bin, DING Kang, Instantaneous frequency estimation based on generalized demodulation smoothed energy separation algorithm [J]. Journal of Vibration Engineering, 2014, 27(2): 281-288.
[16] 皮维, 于德介, 彭富强. 基于多尺度线调频基稀疏信号分解的广义解调方法及其在齿轮故障诊断中的应用[J]. 机械工程学报, 2010, 46(15): 59-64.