Compound fault features separation of rolling element bearing based on the wavelet decomposition and spectrum auto-correlation

被引：11

作者：

机构：

[1] State Key laboratory of Tribology, Tsinghua University

[2] The Sixth Department, The Second Artillery Engineering University

来源：

Chu, F. (chufl@mail.tsinghua.edu.cn) | 1600年 / Chinese Mechanical Engineering Society卷 / 49期

关键词：

Compound fault; Features separation; Rolling element bearing; Spectrum auto-correlation; Wavelet decomposition;

D O I：

10.3901/JME.2013.03.080

中图分类号：

学科分类号：

摘要：

In order to separate the compound fault features from a single-channel vibration, the combination of the wavelet decomposition and spectrum auto-correlation method is proposed, based on the wavelet frame theory. Decomposing the compound fault deduced vibration with orthogonal wavelet basis functions, the spectrum auto-correlation method is applied to sub-signals that reconstructed with different scales vibration respectively. Eliminating the tail phenomenon which existed in the result of time domain auto-correlation, the proposed method possesses a more powerful anti-noise capability and highlights the fault deduced impulse feature with primary energy. Based on the analysis of vibration collected on the test rig of 6220 rolling element bearing with inner and outer race defect, the efficiency of the proposed procedure is validated as well. It is shown that the lesser powerful fault induced impulsive feature is restrained in any decomposed sub signals, which actualizes the separation of the compound fault features. Compared with the combination of wavelet and envelope analysis, the proposed method is more powerful with efficient features separation effect and is valuable for the engineering application. © 2013 Journal of Mechanical Engineering.

引用

页码：80 / 87

页数：7

共 19 条

[1]

Mcfadden P.D., Smith J.D., Vibration monitoring of rolling element bearings by the high-frequency resonance technique: A review, Tribology International, 17, 1, pp. 3-10, (1984)

[2]

Peng Z., Chu F., Application of the wavelet transform in machine condition monitoring and fault diagnostics: A review with bibliography, Mechanical Systems and Signal Processing, 18, 2, pp. 199-221, (2004)

[3]

Zhang J., Feng Z., Chu F., Extraction of rolling bearing fault feature based on time-wavelet energy spectrum, Journal of Mechanical Engineering, 47, 17, pp. 44-49, (2011)

[4]

Antoni J., The spectral kurtosis: A useful tool for characterising non-stationary signals, Mechanical Systems and Signal Processing, 20, 2, pp. 282-307, (2006)

[5]

Antoni J., Randall R.B., The spectral kurtosis: Application to the vibratory surveillance and diagnostics of rotating machines, Mechanical Systems and Signal Processing, 20, 2, pp. 308-331, (2006)

[6]

Barszcz T., Jablonski A., A novel method for the optimal band selection for vibration signal demodulation and comparison with the kurtogram, Mechanical Systems and Signal Processing, 25, 1, pp. 431-451, (2011)

[7]

Urbanek J., Antoni J., Barszcz T., Detection of signal component modulations using modulation intensity distribution, Mechanical Systems and Signal Processing, 28, 0, pp. 399-413, (2012)

[8]

Bachschmid N., Pennacchi P., Vania A., Identification of multiple faults in rotor systems, Journal of Sound and Vibration, 254, 2, pp. 327-366, (2002)

[9]

Patel T.H., Darpe A.K., Coupled bendling-torsional vibration analysis of rotor with rub and crack, Journal of Sound and Vibration, 326, 3-5, pp. 740-752, (2009)

[10]

Jalan A.K., Mohanty A.R., Model based fault diagnosis of a rotor-bearing system for misalignment and unbalance under steady-state condition, Journal of Sound and Vibration, 327, 3-5, pp. 604-622, (2009)

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