Fault detection method for varying rotating speed bearings based on time-frequency squeeze and order analysis

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Journal of Vibration and Shock ›› 2020, Vol. 39 ›› Issue (3) : 205-210.

GAO Guanqi, HUANG Weiguo, LI Ning, LI Shijun, SHI Juanjuan, JIANG Xingxing, ZHU Zhongkui

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Abstract

Order analysis is one of the main methods to realize fault feature extraction of rotating machinery equipment under condition of varying rotating speed, and its key is correct acquisition of rotating speed information.The traditional order analysis method is using a tachometer or other equipment to measure rotating speed with high cost and bad noise resistance, while the rotating speed estimation method based on time-frequency analysis is simple to operate with better robustness and correctness.Here, a rotating speed estimation method was proposed based on time-frequency squeeze, and this method didn’t depend upon redundant equipment, it realized rotating speed estimation and feature extraction through time-frequency squeeze and resampling order analysis to diagnose bearing’s faults.The analysis results using the proposed method were compared with those using the traditional time-frequency method and those using the calculation order analysis to verify the feasibility and effectiveness of the proposed method.Both simulated and test signals’ analysis results showed that the proposed method has better time-frequency aggregation property and robustness than the traditional method does, and its analysis results’ accuracy is higher.

Key words

varying rotating speed / time-frequency squeeze / rotating speed estimation / order analysis / fault diagnosis

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GAO Guanqi, HUANG Weiguo, LI Ning, LI Shijun, SHI Juanjuan, JIANG Xingxing, ZHU Zhongkui. Fault detection method for varying rotating speed bearings based on time-frequency squeeze and order analysis[J]. Journal of Vibration and Shock, 2020, 39(3): 205-210

References

[1] Cheng W, Gao R X, Wang J, et al. Envelope deformation in computed order tracking and error in order analysis[J]. Mechanical Systems and Signal Processing, 2014, 48(1-2): 92-102.
[2] Zimroz R, Bartelmus W, Barszcz T, et al. Diagnostics of bearings in presence of strong operating conditions non-stationarity—A procedure of load-dependent features processing with application to wind turbine bearings[J]. Mechanical Systems and Signal Processing, 2014, 46(1): 16-27.
[3] 郭瑜, 秦树人. 旋转机械非稳定信号的伪转速跟踪阶比分析[J]. 振动与冲击, 2004, 23(1):61-64.
GUO Yu, QIN Shu-ren. Pseudo-speed tracking order analysis for non-stationary vibration signals of rotating machinery [J]. Journal of vibration and shock, 2004, 23(1): 61-64.
[4] Fyfe K R, Munck E D S. Analysis of computed order tracking[J]. Mechanical Systems and Signal Processing, 1997, 11(2): 187-205.
[5] Bossley K M, Mckendrick R J, Harris C J, et al. Hybrid computed order tracking[J]. Mechanical Systems and Signal Processing, 1999, 13(4): 627-641.
[6] 刘亭伟, 郭瑜, 李斌,等. 基于谱峭度的滚动轴承故障包络阶比跟踪分析[J]. 振动与冲击, 2012, 31(17): 149-153.
LIU Ting-wei, GUO Yu, LI Bin, et al. Envelope order tracking analysis for rolling element bearing faults based on spectral kurtosis [J]. Journal of vibration and shock, 2012, 31(17): 149-153.
[7] 赵德尊, 李建勇, 程卫东,等. 变转速下基于广义解调算法的滚动轴承故障诊断[J]. 振动工程学报, 2017, 30(5): 865-873.
ZHAO De-zun, LI Jian-yong, CHENG Wei-dong, et al. Rolling element bearing fault diagnosis based on generalized demodulation algorithm under variable rotational speed [J]. Journal of vibration engineering, 2017, 30(5): 865-873.
[8] 程卫东, 赵德尊, 刘东东. 基于故障特征系数模板的变转速滚动轴承故障诊断[J]. 振动与冲击, 2017, 36(7): 123-129.
CHENG Wei-dong, ZHAO De-zun, LIU Dong-dong. Rolling element bearing fault diagnosis based on fault characteristic coefficient template under time-varying rotating speed [J]. Journal of vibration and shock, 2017, 36(7): 123-129.
[9] Emresoy M K, EI-Jaroudi A. Iterative instantaneous frequency estimation and adaptive spectrogram[J]. Signal Processing, 1998, 64(2): 157-165.
[10] Tary J B, Herrera R H, Han J, et al. Spectral estimation—What is new? What is next?[J]. Reviews of Geophysics, 2014, 52(4): 723-749.
[11] Wang T, Liang M, Li J, et al. Bearing fault diagnosis under unknown variable speed via gear noise cancellation and rotational order sideband identification[J]. Mechanical Systems and Signal Processing, 2015, 62-63(1): 30-53.
[12] 刘东东, 程卫东, 万广通. 基于故障特征趋势线模板的滚动轴承故障诊断[J]. 机械工程学报, 2017, 53(9): 83-91.
LIU Dong-dong, CHEN Wei-dong, WAN Guang-tong. Bearing Fault Diagnosis Based on Fault Characteristic Trend Template [J]. Journal of mechanical engineering, 2017, 53(9): 83-91.
[13] 郭瑜, 秦树人, 汤宝平等. 基于瞬时频率估计的旋转机械阶比跟踪[J]. 机械工程学报, 2003, 39(3): 32-36.
GUO Yu, QIN Shu-ren, TANG Bao-ping, et al. The order tracking of rotating machinery based on instantaneous frequency estimation [J]. Journal of mechanical engineering, 2003, 39(3): 32-36.
[14] 曹书峰, 朱忠奎, 黄伟国,等. 基于时频融合的转速估计及轴承故障特征提取研究[J]. 振动与冲击, 2013, 32(18): 174-178.
CAO Shu-feng, ZHU Zhong-kui, HUANG Wei-guo, et al. Speed estimation based on time-frequency fusion and its application in feature extraction of bearing fault [J]. Journal of vibration and shock, 2013, 32(18): 174-178.
[15] Daubechies I, Lu J, Wu H T. Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool[J]. Applied and Computational Harmonic Analysis, 2011, 30(2): 243-261.
[16] Yu G, Yu M, Xu C. Synchroextracting Transform[J]. IEEE Transactions on Industrial Electronics, 2017, 64(10): 8042-8054.