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Prognostic Technique for Ball Bearing Damage

볼 베어링 손상 예측진단 방법

  • Received : 2012.12.28
  • Accepted : 2013.09.28
  • Published : 2013.11.01

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

References

  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. https://doi.org/10.1006/mssp.2000.1301
  2. Kotzalas, M. N. and Harris, T. A., 2001, "Fatigue Failure Progression in Ball Bearings," Trans. of the ASME, Vol. 123, pp. 238-242. https://doi.org/10.1115/1.1308013
  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. https://doi.org/10.1006/mssp.2002.1483
  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. https://doi.org/10.3795/KSME-A.2010.34.11.1681
  7. Orchard, M., Wu, B. and Vachtsevanos, G., 2005, "A Particle Filtering-based Framework for Failure Prognosis," Proceedings of WTC2005, pp. 1-2.

Cited by

  1. vol.17, pp.2, 2014, https://doi.org/10.5293/kfma.2014.17.2.073