Residual life prediction based on dynamic weighted Markov model and particle filtering

In order to improve the prediction accuracy of non-Gaussian data and build reasonably the prediction model, a novel residual life prediction method is proposed. A dynamic weighted Markov model is constructed by real time data and historical data, and the residual life is predicted by particle filter. The particles of the state vector are predicted and updated instantaneously using particle filter. The probability distribution of the predicted value is estimated by the updated particles. The residual life can be predicted using the set threshold of the state. This method improves the accuracy of residual life prediction. Finally, the advantage of this proposed method was shown experimentally using the bearings’ full cycle data.