Stochastic-resonance based fault diagnosis for rolling element bearings subjected to low rotational speed

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Nov 1, 2020
Agusmian Partogi Ompusunggu Steven Devos Frederik Petre

Abstract

Despite been introduced about more than 30 years ago, the Stochastic-Resonance (SR) theory has only been gaining considerable attention in the field of condition based maintenance (CBM) in recent years. SR is a nonlinear physical phenomenon where weak signals (i.e. signals with low signalto- noise ratio) can be enhanced by a cooperative interaction of noise and periodic excitation (stimulus) in particular nonlinear systems, e.g. “bistable” systems. In other words, SR serves as a non-linear filter that can amplify a (periodic) signal of interest heavily masked by a large background noise by adjusting both the parameters of the nonlinear bi-stable system and the intensity of artificially generated noise to be added to the measurement signal. This paper discusses an improvement of SR filtering with multi scale noise tuning recently published for multi-fault diagnosis of a rolling element bearing subjected to low rotational speed and low load. Prior to application of the SR filtering, gear-related signals are removed from a measured acceleration vibration signal (i.e. signal pre-whitening) by means of the cepstra-based discrete components removal. The pre-whitened signal is further processed by employing an optimized SR filter in which the filter parameters are pre-determined based on the faulty data. Finally, features defined as the ratio between the peaks around the corresponding bearing fault frequencies and the background noise level are extracted from the spectrum of the output signal obtained from the SR filtering. A number of experiments have been carried out on a gearbox dynamics simulator with healthy and faulty bearings in order to demonstrate and verify the effectiveness of the proposed approach for bearing fault detection under low shaft rotational speed (348 rpm) and low load (1.2 Nm). For comparison purpose, the experimental data have been analyzed both with the well known “envelope” method and the proposed framework. The experimental results show that the proposed framework outperforms the envelope method.

Abstract 372 | PDF Downloads 238

##plugins.themes.bootstrap3.article.details##

Keywords

diagnosis, Rolling element bearing, stochastic resonance, multi-scale noise tuning

References
Antoni, J., & Randall, R. (2006). The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines. Mechanical Systems and Signal Processing, 20(2), 308 - 331.
Benzi, R., Sutera, A., & Vulpiani, A. (1981). The mechanism of stochastic resonance. Journal of Physics A: Mathematical and General, 14(11), L453.
Flandrin, P. (1992). Wavelet analysis and synthesis of fractional brownian motion. Information Theory, IEEE Transactions on, 38(2), 910-917.
Gammaitoni, L., H¨anggi, P., Jung, P., & Marchesoni, F. (1998, Jan). Stochastic resonance. Rev. Mod. Phys., 70, 223–287.
He, H.-L., Wang, T.-Y., Leng, Y.-G., Zhang, Y., & Li, Q. (2007). Study on non-linear filter characteristic and engineering application of cascaded bistable stochastic resonance system . Mechanical Systems and Signal Processing, 21(7), 2740 - 2749.
He, Q., Wang, J., Liu, Y., Dai, D., & Kong, F. (2012). Multiscale noise tuning of stochastic resonance for enhanced fault diagnosis in rotating machines . Mechanical Systems and Signal Processing, 28(0), 443 - 457.
Jung, P., & H¨anggi, P. (1991, Dec). Amplification of small signals via stochastic resonance. Phys. Rev. A, 44, 8032–8042.
Kim, Y.-H., Tan, A., Mathew, J., & Yang, B.-S. (2006). Condition Monitoring of Low Speed Bearings: A Comparative Study of the Ultrasound Technique Versus Vibration Measurements. In J. Mathew, J. Kennedy, L. Ma,
A. Tan, & D. Anderson (Eds.), Engineering asset management (p. 182-191). Springer London.
Kosse, V., & Tan, A. (2006). Development of Testing Facilities for Verification of Machine Condition Monitoring Methods for Low Speed Machinery. In J. Mathew, J. Kennedy, L. Ma, A. Tan, & D. Anderson (Eds.), Engineering asset management (p. 192-197). Springer London.
Lei, Y., Han, D., Lin, J., & He, Z. (2013). Planetary gearbox fault diagnosis using an adaptive stochastic resonance method. Mechanical Systems and Signal Processing, 38(1), 113 - 124.
Leng, Y., Leng, Y., Wang, T., & Guo, Y. (2006). Numerical analysis and engineering application of large parameter stochastic resonance. Journal of Sound and Vibration, 292(35), 788 - 801.
Li, J., Chen, X., Du, Z., Fang, Z., & He, Z. (2013). A new noise-controlled second-order enhanced stochastic resonance method with its application in wind turbine drivetrain fault diagnosis. Renewable Energy, 60(0), 7 - 19.
Li, J., Chen, X., & He, Z. (2013). Adaptive stochastic resonance method for impact signal detection based on sliding window. Mechanical Systems and Signal Processing, 36(2), 240 - 255.
Lin, T., Kim, E., & Tan, A. (2013). A practical signal processing approach for condition monitoring of low speed machinery using Peak-Hold-Down-Sample algorithm. Mechanical Systems and Signal Processing, 36(2), 256 - 270.
McNamara, B., & Wiesenfeld, K. (1989, May). Theory of stochastic resonance. Phys. Rev. A, 39, 4854–4869.
Randall, R., & Antoni, J. (2011). Rolling element bearing diagnostics - A tutorial . Mechanical Systems and Signal Processing, 25(2), 485 - 520.
Randall, R., & Sawalhi, N. (2011). Use of the cepstrum to remove selected discrete frequency components from a time signal. In T. Proulx (Ed.), Rotating machinery, structural health monitoring, shock and vibration, volume 5 (p. 451-461). Springer New York.
Tan, J., Chen, X., Wang, J., Chen, H., Cao, H., Zi, Y., & He, Z. (2009). Study of frequency-shifted and re-scaling stochastic resonance and its application to fault diagnosis. Mechanical Systems and Signal Processing, 23(3), 811 - 822.
Wu, X., Jiang, Z.-P., & Repperger, D. (2006). Enhancement of stochastic resonance by tuning system parameters and adding noise simultaneously. In American control conference, 2006 (p. 6 pp.-).
Xu, B., Li, J., & Zheng, J. (2003). How to tune the system parameters to realize stochastic resonance. Journal of Physics A: Mathematical and General, 36(48), 11969.
Section
Technical Papers