Extended SVD packet and its application in wheelset bearing fault diagnosis of high-speed train

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Journal of Vibration and Shock ›› 2020, Vol. 39 ›› Issue (5) : 45-56.

HUANG Chenguang1, LIN Jianhui1, YI Cai2, HUANG Yan1, JIN Hang1

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Abstract

Combining decomposed structure of the multi-resolution SVD package and singular value distribution characteristics of a rolling bearing fault signal’s Hankel matrix, the extended singular value decomposition (SVD) package was proposed.The core of this method included matrix recurrence construction and matrix reconstruction.Component signal energy was taken as an index to propose the screening criterion of effective component signals.Then based on the proposed criterion, a fast algorithm for the extended SVD packet was further proposed.The simulation results showed that the extended SVD packet has good decomposition ability for resonance frequency band components in a signal; the method has a strong robustness and greatly improves modal aliasing appearing in the SVD packet.The test data for high-speed train’s wheelset bearing were used to verify the proposed method.The results showed that this method can effectively separate different resonant frequency band signals in high speed train wheelset bearing compound fault signals, and perform envelope analysis for screened effective component signals to effectively extract different types fault feature frequencies and their harmonics; compared to the SVD package, the proposed method makes resonance bands’ aggregation property and faults’ characterizing ability be significantly improved.

Key words

wheelset bearing / Hankel matrix / singular value decomposition (SVD) / extended SVD packet

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HUANG Chenguang1, LIN Jianhui1, YI Cai2, HUANG Yan1, JIN Hang1. Extended SVD packet and its application in wheelset bearing fault diagnosis of high-speed train[J]. Journal of Vibration and Shock, 2020, 39(5): 45-56

References

[1] Ding J. Fault detection of a wheelset bearing in a high-speed train using the shock-response convolutional sparse-coding technique[J]. Measurement, 2018, 117(1):108-124.
[2] Duan Z, Wu T, Guo S, et al. Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: a review[J]. International Journal of Advanced Manufacturing Technology, 2018, 96(1-4):803-819.
[3] 张锐戈. 滚动轴承振动信号非平稳、非高斯分析及故障诊断研究[D]. 西安电子科技大学, 2014.
Zhang R. Vibration Signal Non-Station, Non-Gaussian Analysis and Fault Diagnosis Based on Rolling Element Bearings[D]. XIDIAN UNIVERSITY, 2014.
[4] 何刘, 林建辉, 刘新厂,等. 万向轴动不平衡检测的改进 DTCWT-SVD 方法[J]. 振动与冲击, 2016, 35(22):142-151.
He Liu, Lin Jianhui, Liu Xinchang, et al. Detection of the dynamic imbalance of cardan shaft by applying an improved DTCWT-SVD[J]. Journal of Vibration and Shock, 2016, 35(22):142-151.
[5] 曾鸣, 杨宇, 郑近德,等. μ-SVD降噪算法及其在齿轮故障诊断中的应用[J]. 机械工程学报, 2015, 51(3):95-103.
Zeng Ming, Yang Yu, Zheng Jinde, et al. μ-SVD Based Denoising Method and Its Application to Gear Fault Diagnosis[J]. Journal of Mechanical Engineering, 2015, 51(3):95-103.
[6] Zhao X, Ye B. Selection of effective singular values using difference spectrum and its application to fault diagnosis of headstock[J]. Mechanical Systems & Signal Processing, 2011, 25(5):1617-1631.
[7] 崔伟成, 许爱强, 李伟,等. 基于拟合误差最小化原则的奇异值分解降噪有效秩阶次确定方法[J]. 振动与冲击, 2017, 36(3):132-137.
Cui Weicheng, Xu Aiqiang, Li Wei, et al. A new method for determining effective rank order of singular value decomposition denoising based on fitting error minimum principle[J]. Journal of Vibration and Shock, 2017, 36(3):132-137.
[8] 赵学智, 叶邦彦, 陈统坚. 基于小波-奇异值分解差分谱的弱故障特征提取方法[J]. 机械工程学报, 2012, 48(7):37-48.
Zhao Xuezhi, Ye Bangyan, Chen Tongjian. Extraction Method of Faint Fault Feature Based on Wavelet-SVD Difference Spectrum[J]. Journal of Mechanical Engineering, 2012, 48(7):37-48.
[9] 赵洪山, 郭双伟, 高夺. 基于奇异值分解和变分模态分解的轴承故障特征提取[J]. 振动与冲击, 2016, 35(22):183-188.
Zhao Hongshan, Guo Shuangwei, Gao Duo. Fault feature extraction of bearing faults based on singular value decomposition and variational modal decomposition[J]. Journal of Vibration and Shock, 2016, 35(22):183-188.
[10] Golafshan R, Sanliturk K Y. SVD and Hankel matrix based de-noising approach for ball bearing fault detection and its assessment using artificial faults[J]. Mechanical Systems & Signal Processing, 2016, 70–71(1):36-50.
[11] Zhao X, Ye B. Singular value decomposition packet and its application to extraction of weak fault feature[J]. Mechanical Systems & Signal Processing, 2016, 70–71(1):73-86.
[12] 张亢, 程军圣. 基于局部均值分解和峭度图的滚动轴承包络分析方法[J]. 航空动力学报, 2015, 30(12):3043-3050.
Zhang Kang, Cheng Junsheng. Roller bearing envelope analysis method based on local mean decomposition and kurtogram[J]. Journal of Aerospace Power, 2015, 30(12):3043-3050.
[13] 戈卢布 G H, 范洛恩 C F. 矩阵计算[M]. 袁亚湘, 译. 北京: 科学出版社, 2001.
[14] Gilles J. Empirical Wavelet Transform[J]. IEEE Transactions on Signal Processing, 2013, 61(16):3999-4010.
[15] 江星星, 李舜酩. 多共振频带自适应检测的轴承微弱故障诊断方法[J]. 电子测量与仪器学报, 2016, 30(4):526-533.
Jiang Xingxing, Li Shunming. Bearing weak fault diagnosis method based on adaptive detection of multi-resonance bands[J]. Journal of electronic measurement and instrumentation, 2016, 30(4):526-533.
[16] 钱征文, 程礼, 李应红. 利用奇异值分解的信号降噪方法[J]. 振动、测试与诊断, 2011, 31(4):459-463.
Qian Zhengwen, Cheng Li, Li Yinghong. Noise reduction method based on singular value decomposition[J]. Journal of Vibration, Measurement & Diagnosis, 2011, 31(4):459-463.
[17] PIETRO BONIZZI, JOËL M. H. KAREL, OLIVIER MESTE, et al. SINGULAR SPECTRUM DECOMPOSITION: A NEW METHOD FOR TIME SERIES DECOMPOSITION[J]. Advances in Adaptive Data Analysis, 2015, 6(04):107-109.
[18] Antoni J. Fast computation of the kurtogram for the detection of transient faults[J]. Mechanical Systems & Signal Processing, 2007, 21(1):108-124.
[19] Wang D. Some further thoughts about spectral kurtosis, spectral L2/L1 norm, spectral smoothness index and spectral Gini index for characterizing repetitive transients[J]. Mechanical Systems & Signal Processing, 2018, 108(1):360-368.
[20] Randall R B, Antoni J. Rolling element bearing diagnostics—A tutorial[J]. Mechanical Systems & Signal Processing, 2011, 25(2):485-520.