Prognostic Technique for Ball Bearing Damage

볼 베어링 손상 예측진단 방법

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


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.


Bearing;Damage;Prognostics;Particle Filter


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