Lebesgue sampling-based fault diagnosis and prognosis (LSFDP) is developed with the advantage of less computation requirement and smaller uncertainty accumulation. Same as other diagnostic and prognostic approaches, the accuracy and precision of LS-FDP are significantly influenced by the diagnostic and prognostic models. The predicted results will show great discrepancy with the real remaining useful life (RUL) in applications if the model is not accurate. In addition, the fixed model parameters cannot accommodate the varying stress factors that affect the fault dynamics. To address this problem, the parameters in the models are treated as time-varying ones and are adjusted online to accommodate changing dynamics. In this paper, a recursive least square (RLS) based method with a forgetting factor is employed to make the diagnostic and prognostic models online adaptive in LS-FDP. The design and implementation of LS-FDP are based on a particle filtering algorithm and are illustrated with experiments of Li-ion batteries. The experimental results show that the performance of LS-FDP with model adaptation is improved on both battery capacity estimation and RUL prediction.
How to Cite
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