Abstract
This paper proposes a novel remaining useful life (RUL) prediction approach with application to predictive maintenance for stochastic degrading systems. By modeling the degradation state with a set of weighted basis functions, it can achieve a more flexible representation for modeling the nonlinear behaviors within degradation process. Then, the parameters including the weights is computed by a sparse Bayesian algorithm. After parameter identification, we obtain an explicit form of the RUL distribution based on the proposed degradation process model. Further, the optimal replacement time for predictive maintenance is obtained by minimizing the expected cost rate which is calculated with the aid of RUL distribution. Finally, the effectiveness of the proposed method in RUL prediction and predictive maintenance is verified by a numerical simulation.
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