Bearing Fault Diagnosis using Singular Spectrum Analysis-Based Envelope Detection
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Rolling element bearings are critical components in rotating machinery and it is important to monitor their health and detect their faults in early stage during their operations. The vibration energy generated by the faults in rolling element bearings is usually small comparing to that of other rotating components such as rotors/shafts and gears in mechanical systems. Envelope analysis is a widely used method in bearing fault diagnosis. The major challenge in envelope analysis is the identification of resonance frequency band for narrow-band filtering before applying envelope operation. In this paper, a method combining singular spectrum analysis (SSA) and envelope analysis is proposed for diagnosing the rolling element bearing faults. The SSA is utilized to decompose the bearing vibration signal into a set of eigentriples (principle components), and then a subset of the eigentriples that encompass the dominant variation in the original signal is selected for signal reconstruction. Envelope analysis is then applied to the reconstructed signal to extract the modulation information that caused by the bearing faults. The proposed SSA-based envelope analysis is applied to the data sets of the bearing data center at Case Western Reserve University (CWRU), and the results are compared with that of the widely used Kurtogram-based envelope analysis. The results show that the SSA-based envelope analysis is more effective than the Kurtogrambased envelope analysis in bearing fault diagnosis.
##plugins.themes.bootstrap3.article.details##
PHM
Antoni, J. (2006). The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines. Mech. Syst. Signal Process. Vol. 20, pp. 282- 307. doi:10.1016/j.ymssp.2004.09.001
Antoni, J., & Randall R. B. (2006). The spectral kurtosis: a useful tool for characterising non-stationary signals. Mech. Syst. Signal Process. Vol. 20, pp. 308-331. doi:10.1016/j.ymssp.2004.09.002
Bearing Data Centre, Case Western Reserve University, Available: http://csegroups.case.edu/bearingdatacenter/home
Broomhead, D.S., & King, G.P. (1986). Extracting qualitative dynamics from experimental data. Physica D. vol. 20, pp. 217–236. doi: 10.1016/0167-2789(86) 90031-X
Chao, S.-H. & Loh, C.-H. (2014). Application of singular spectrum analysis to structural monitoring and damage diagnosis of bridges. Structure and Infrastructure Engineering: Maintenance, Management, Life-Cycle Design and Performance, vol. 10:6, pp. 708-727, doi: 10.1080/15732479.2012.758643
Golyandina , N., & Korobeynikov, A. (2014). Basic Singular Spectrum Analysis and forecasting with R. Computational Statistics & Data Analysis, vol. 71, pp. 934-954. doi: 10.1016/j.csda.2013.04. 009
Golyandina, N., & Zhigljavsky, A. (2013). Singular Spectrum Analysis for Time Series. Springer. Doi: 10.1007/978-3-642-34913-3
Harris, T.J., & Yuan, H. (2010). Filtering and frequency interpretations of Singular Spectrum Analysis. Physica D vol. 239, pp. 1958–1967. doi:10.1016/j.physd. 2010.07.005
Hassani, H. (2010). Singular spectrum analysis based on the minimum variance estimator. Nonlinear Analysis: Real World Applications, vol. 11, pp. 2065–2077. doi: 10.1016/j.nonrwa.2009.05.009
Khan, M. A. R., & Poskitt, D. S. (2013). A note on window length selection in singular spectrum analysis. Australian & New Zealand Journal of Statistics. vol. 55(2), pp. 87–108. doi: 10.1111/anzs.12027
Khelifa, S., Kahlouche, S., & Belbachir, M. F. (2012). Signal and noise separation in time series of DORIS station coordinates using wavelet and singular spectrum analysis. Comptes Rendus Geoscience, vol. 344, pp. 334-348., doi: 10.1016/j.crte.2012.05.003
Kilundu, B., Chiementin, X., & Dehombreux, P. (2011). Singular spectrum analysis for bearing defect detection. Journal of Vibration and Acoustics, Vol. 133(5), pp. 051007-051007-7. doi: 10.1115/1.4003938
Kilundu, B., Dehombreux, P. & Chiementin, X. (2011). Tool wear monitoring by machine learning techniques and singular spectrum analysis. Mech. Syst. Signal Process, vol. 25, pp. 400–415 doi:10.1016/j.ymssp. 2010.07.014
Liu, T., Chen, J. & Dong, G. M. (2013). Singular spectrum analysis and continuous hidden Markov model for rolling element bearing fault diagnosis. Journal of Vibration and Control, vol. 21(8). pp. 1506-1521. doi: 10.1177/1077546313496833
Muruganatham, B., Sanjith, M.A., Krishnakumar, B., & Murty, S.A.V. S. (2013). Roller element bearing fault diagnosis using singular spectrum analysis. Mech. Syst. Signal Process, vol. 35, pp. 150–166. doi: 10.1016/j. ymssp.2012.08.019
PHM Challenge 2009 Data Sets: http://www.phmsociety.org/references/datasets
Randall R. B., & Antoni, J. (2011). Rolling element bearing diagnostics – A Tutorial. Mech. Syst. Signal Process. Vol. 25, pp. 485-520. doi:10.1016/j.ymssp.2010.07.017
Salgado, D.R., & Alonso, F.J. (2006). Tool wear detection in turning operations using singular spectrum analysis. Journal of Materials Processing Technology, vol. 171, pp. 451–458. doi:10.1016/j.jmatprotec.2005.08.005
Sanei, S., & Hassani, H. (2016). Singular spectrum analysis of biomedical signals. CRC Press, Taylor & Francis Group.
Viljoen, H. & Nel, D.G. (2010). Common singular spectrum analysis of several time series. Journal of Statistical Planning and Inference vol. 140, pp. 260-267. doi:10.1016/j.jspi.2009.07.009
Shi, D.F., Wang W. J., & Qu, L. S.(2004). Defect Detection for Bearings Using Envelope Spectra of Wavelet Transform. J. Vib. Acoust, 126(4), pp. 567-573. doi:10.1115/1.1804995
Smith, W. A., & Randall R. B. (2015). Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study. Mech. Syst. Signal Process. Vol. 64-65, pp. 100-131. doi: 10.1016/j.ymssp.
2015.04.021
Vautard, R. & Ghil, M. (1989). Singular spectrum analysis in nonlinear dynamics, with applications to paleoclimatic time series. Physica D, Vol. 35(3), pp. 395-424. doi: 10.1016/0167-2789(89)90077-8
Wade A. Smith, W.A., & Randall, R.B. (2015). Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study. Mech. Syst. Signal Process, vol. 64–65, pp. 100–131. doi: 10.1016/j.ymssp.2015.04.021
Wang, D., Tse, P. W., & Tsui, K. L. (2013). An enhanced Kurtogram method for fault diagnosis of rolling element bearings. Mech. Syst. Signal Process, vol. 35, pp. 176-199. doi: 10.1016/j.ymssp. 2012.10.003
Zhao, X. Z. & Ye, B.Y. (2011). Selection of effective singular values using difference spectrum and its application to fault diagnosis of headstock. Mech. Syst. Signal Process, vol. 25, pp. 1617-1631. doi:10.1016/j.ymssp. 2011.01. 003