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Adaptive Particle Filter-Based Approach for RUL Prediction Under Uncertain Varying Stresses With Application to HDD

In recent years, the estimation of the remaining useful life (RUL) has become an increasingly important topic. Existing RUL estimation studies mainly focus on linear degradation cases or degradation processes that can be linearized. A few nonlinear degradation models often rely on a training process based on a batch of samples obtained from the same population. Consequently, large bias or uncertainties may often occur under varying stress conditions. To address this problem, this article proposes a prognostic approach based on an adaptive particle filter (PF) to predict the RUL of the dynamic degradation systems using system degradation records. First, a nonlinear degradation model based on the fusion of an exponential item and a power law wear model were derived to capture the wear process under varying stress conditions. Second, the PF method was used to update the model parameters by treating the parameters as hidden state variables. Third, an adaptive strategy was derived based on the expectation–maximization algorithm and particle smoother algorithm to recursively update the hidden variables. Finally, an actual magnetic head wear dataset obtained from an actual manufacturing plant is used to verify the effectiveness of the proposed approach. The results reveal that the proposed approach significantly improves the prediction accuracy compared with the PF-based approach and extends Kalman-filter-based approach.