A particle filtering and kernel smoothing-based approach for new design component prognostics

This work addresses the problem of predicting the Remaining Useful Life (RUL) of components for which a mathematical model describing the component degradation is available, but the values of the model parameters are not known and the observations of degradation trajectories in similar components are unavailable. The proposed approach solves this problem by using a Particle Filtering (PF) technique combined with a kernel smoothing (KS) method. This PF–KS method can simultaneously estimate the degradation state and the unknown parameters in the degradation model, while significantly overcoming the problem of particle impoverishment. Based on the updated degradation model (where the unknown parameters are replaced by the estimated ones), the RUL prediction is then performed by simulating future particles evolutions. A numerical application regarding prognostics for Lithium-ion batteries is considered. Various performance indicators measuring precision, accuracy, steadiness and risk of the obtained RUL predictions are computed. The obtained results show that the proposed PF–KS method can provide more satisfactory results than the traditional PF methods. - Highlights: • A Particle Filtering method for predicting remaining useful life is proposed. • True values of the model parameters are unknown and historical data are unavailable. • A combined estimate of model parameters and remaining useful life is obtained. • Kernel smoothing is used to improve the accuracy and robustness of the estimates. • The method is applied for remaining useful life prediction of Li-ion batteries