Data-Driven Roller Bearing Diagnosis Using Degree of Randomness and Laplace Test



Published Mar 26, 2021
Bo Ling Michael Khonsari Ross Hathaway


In this paper, we present a new diagnosis and prognosis method using the degree of randomness (DoR) measure and Laplace test procedure. The abnormal events are detected based on changes of randomness of vibration signals. The trend of randomness is resulted from faulty components such as roller bearings. We aim at the early detection of semi-failure events through the use of Laplace test statistic which measures the rate changes of abnormal event occurrence. Algorithms are data-driven and capable of making fault detections at its early stages. They have also been integrated into a real-time diagnosis system.

How to Cite

Ling, B. ., Khonsari, M. ., & Hathaway, R. . (2021). Data-Driven Roller Bearing Diagnosis Using Degree of Randomness and Laplace Test. Annual Conference of the PHM Society, 1(1). Retrieved from
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bearings, detection

J. Antoni, R. B. Randall (2006). The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines. Mechanical Systems and Signal Processing, Vol. 20(6), pp. 308 – 331.
H. E. Ascher, T. Y. Lin, D. P. Siewiorek (1992). "Modification of Error Log Analysis: Statistical Modeling and Heuristic Trend Analysis," IEEE Trans. of Reliability, Vol. 41, pp. 599-601, December 1992.
P. Dempsey, D. G. Lewicki, H. J. Decker (2004). Investigation of Gear and Bearing Fatigue Damage Using Debris Particle Distributions. NASA/TM 2004-212883.
X. F. Fan (2007). A joint wavelet lifting and independent component analysis approach to fault detection of rolling element bearings. Smart Materials & Structures Vol.16 (5), pp. 1973-1987.
R. J. Hickey (1983). Majorization, randomness and some discrete distributions, J. Appl. Prob. 20, 897- 902.
J. Ilonen, J. K. Kamarainen, T. Lindh, J. Ahola, H. Kalviainen, J. Partanen (2005). Diagnosis tool for motor condition monitoring. IEEE Transactions on Industry Applications, Volume 41, Issue 4, July- Aug. Page(s):963 – 971.
A. Lempel, J. Ziv. (1976). On the Complexity of Finite-Sequences, IEEE Trans. on Inf. Theory, Vol. IT-22, No. 1, Jan. 1976.
B. Ling, M. Khonsari, A. Mesgarnejad, R. Hathaway (2009). Online Coated Ball Bearing Health Monitoring Using Degree of Randomness and Hidden Markov Model, IEEE Aerospace Conference, March 2009.
L. E. Morando (1996). Technology Overview: Shock Pulse Method. Joint Oil Analysis Program. Pensacola Fltechnical Support Center, ADP010177.
R. R. Obaid, T. G. Habetler, J. R. Stack (2003). Stator current analysis for bearing damage detection in induction motors. Proceeding of 4th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, pp. 182 - 187.
H. Qiu, Jay Lee, Jing Lin, Gang Yu (2006). “Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics,” Journal of Sound and Vibration 289 (2006) 1066–1090).
M. J. Roemer, C. S. Byington (2007). Prognostics and Health Management Software For Gas Turbine Engine Bearings. Proceedings of the ASME Turbo Expo. New York: ASME.
J. R. Stack, R. G. Harley, T. G. Habetler (2004). An amplitude modulation detector for fault diagnosis in rolling element bearings. IEEE Transactions on Industrial Electronics, vol. 51 (5), pp. 1097-1102.
P. W. Tse, Y. H. Peng, R. Yam (2001). Wavelet Analysis and Envelope Detection for Rolling Element Bearing Fault Diagnosis—Their Effectiveness and Flexibilities. Journal of vibration and acoustics, Vol. 123 (3), pp. 303 - 310.
O. Watanabe (1992). Kolmogorov Complexity and Computational Complexity, Springer-Verlag, New York, 1992.
Y. Yang, D. J. Yu, J. S. Cheng (2007). A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM. Measurement Vol. 40(9-10), pp. 943 -950.
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