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RUL-Prediction

A Two-stage Data-driven Based Prognostic Approach for Bearing Degradation

This is a solution to the IEEE PHM 2012 Prognostic Challenge. It focused on the estimation of the Remaining Useful Life (RUL) of ball bearings, a critical problem among industrial machines, strongly affecting availability, security and cost effectiveness of mechanical systems.

We use a two-stage process for predicting the RUL using a single statistic derived from 14 time-domain features of the observed vibration signals by modelling the degradation process as a Wiener process and continuously updating prior parameters of the underlying linear state-space model of degradation.

  • Multiple time-domain features were fused into one index by calculating the Mahalanobis distance from a known healthy state.
  • The Wiener process was used to model the degradation process of the ball bearings
  • The Mean and Variance of the drift coefficient used Wiener process was predicted using the Kalman filter
  • The Expectation Maximization (EM) algorithm was used to estimate the unknown parameters which in turn are used in the Kalman Filter
  • Rauch-Tung-Striebel (RTS) Smoother is used for fixed interval smoothing, taking into consideration both, the past and future values to predict the current one.

Training

train-flow

Testing

Current code has commented the eqn (9) and KF additional update

train-flow

References

  1. A Two-stage Data-driven Based Prognostic Approach for Bearing Degradation Problem by Yu Wang, Yizhen Peng, Yanyang Zi, Xiaohang Jin, Kwok-Leung Tsui
  2. A Wiener-process-based degradation model with a recursive filter algorithm for remaining useful life estimation by Xiao-Sheng Si, Wenbin Wang, Chang-Hua Hu, Mao-Yin Chen, Dong-Hua Zhou

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