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http://dx.doi.org/10.3795/KSME-A.2013.37.11.1315

Prognostic Technique for Ball Bearing Damage  

Lee, Do Hwan (KHNP Central Research Institute)
Kim, Yang Seok (KHNP Central Research Institute)
Publication Information
Transactions of the Korean Society of Mechanical Engineers A / v.37, no.11, 2013 , pp. 1315-1321 More about this Journal
Abstract
This study presents a prognostic technique for the damage state of a ball bearing. A stochastic bearing fatigue defect-propagation model is applied to estimate the damage progression rate. The damage state and the time to failure are computed by using RMS data from noisy acceleration signals. The parameters of the stochastic defect-propagation model are identified by conducting a series of run-to-failure tests for ball bearings. A regularized particle filter is applied to predict the damage progression rate and update the degradation state based on the acceleration RMS data. The future damage state is predicted based on the most recently measured data and the previously predicted damage state. The developed method was validated by comparing the prognostic results and the test data.
Keywords
Bearing; Damage; Prognostics; Particle Filter;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Li, Y., Kurfess, T. R. and Liang S. Y., 2000, "Stochastic Prognostics for Rolling Element Bearings," Mechanical Systems and Signal Processing, Vol. 14, pp. 747-762.   DOI   ScienceOn
2 Kotzalas, M. N. and Harris, T. A., 2001, "Fatigue Failure Progression in Ball Bearings," Trans. of the ASME, Vol. 123, pp. 238-242.   DOI   ScienceOn
3 Bolander, N., Qiu, H., Eklund, N., Hindle, E. and Rosenfeld, T., 2009, "Physics-based Remaining Useful Life Prediction for Aircraft Engine Bearing Prognosis," Annual Conference of the Prognostics and Health Management Society, pp. 1-9.
4 Simon, D., 2006, Optimal State Estimation: Kalman, $H{\infty}$, and Nonlinear Approaches, Wiley-Interscience.
5 Qiu, J., Set, B. B., Liang, S. Y. and Zhang, C., 2002, "Damage Mechanics Approach for Bearing Lifetime Prognostics," Mechanical Systems and Signal Processing, Vol. 16, pp. 817-29.   DOI   ScienceOn
6 Kim, Y. S., Lee, D. H. and Kim S. K., 2010, "Fault Classification for Rotating Machinery Using Support Vector Machines with Optimal Features Corresponding to Each Fault Type," Trans. Korean Soc. Mech. Eng. A, Vol. 34, No. 11, pp. 1681-1689.   과학기술학회마을   DOI   ScienceOn
7 Orchard, M., Wu, B. and Vachtsevanos, G., 2005, "A Particle Filtering-based Framework for Failure Prognosis," Proceedings of WTC2005, pp. 1-2.