Comparison of Resampling Algorithms for Particle Filter based Remaining Useful Life Estimation

Due to the high performance on state tracking and predicting, particle filter (PF) algorithm has been utilized for diagnosis and prognosis in a variety of areas. Especially, PF can provide uncertainty representation and management on estimating the remaining useful life (RUL) of components and systems. However, particle degeneracy phenomenon limits its performance and application in most of the situations. Therefore, several re-sampling algorithms are proposed to alleviate this problem. Thus, different re-sampling algorithms should be focused and studied for the adaptability and applicability in RUL estimation. This work aims to compare the capabilities of different re-sampling algorithms and evaluate the performance in lithium-ion battery RUL prediction. Four re-sampling algorithms including multinomial re-sampling, residual re-sampling stratified re-sampling and systematic re-sampling are involved and analyzed. Actual battery test data sets from NASA PCoE are used to conduct experiments for evaluation and comparison. Moreover, some quantitative analysis metrics are applied to compare the results of battery RUL estimation.