Abstract
Dynamic loadings encountered by clutch motor of an Automated manual transmission (AMT) passenger vehicle require apposite assessment of nonlinear characteristic of the degrading system to accurately predict the remaining useful life (RUL) for implementation of condition based maintenance (CBM) schedule. The present study involves the application of three variants of particle filter (PF) with various resampling techniques to account for heavy tailed observations and non-Gaussian characteristic of noise to improve the accuracy of RUL estimation. Degradation dataset was generated using accelerated life cycle test (ALCT) on hardware-in-loop (HIL) laboratory of a large Indian automotive company. A comparative analysis of the results showed 91.5, 93.2, and 94.9% enhancement in the efficacy of RUL estimation due to canonical, unscented, and improved unscented particle filters (i-UPF) vis-a-vis the ordinarily fitted exponential degradation model.
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