A holistic comparison of the different resampling algorithms for particle filter based prognosis using lithium ion batteries as a case study

Abstract Prognostic health management (PHM) is a critical and essential aspect of any robust maintenance program in the manufacturing industry for early failure detection and prediction of the remaining useful life (RUL) for the entire system or for a component (sub-system) whose condition is being monitored in real-time. In recent years, a lot of research has been done on developing better performing prognostic algorithms for RUL prediction with the “particle filter (PF)” framework being the most widely used amongst them. To address the problems of particle degeneracy and particle impoverishment, several adaptations of standard particle filters have been proposed by improvising the resampling strategies. However, the efficacy of these algorithms is assessed only under specific conditions involving relatively clean degradation data (low noise), large training data sets and limited degradation patterns (mostly linear or “almost” linear). The purpose of this study is to make a comparison of four most frequently used resampling strategies: Multinomial resampling, Stratified resampling, Systematic resampling and Residual Systematic resampling for lithium-ion battery RUL prediction. They are similar in terms of operation but differ only in the way the ordered sequence of random numbers is generated for resampling thus enabling a standardized comparison in terms of computational complexity of O(N). The robustness of these resampling techniques is tested by adding 50 dB of noise to the measurement data and by considering three different time instants at different stages of the device lifecycle for prediction with different amount of training data. We use the mean squared deviation (MSD), relative accuracy (RA), execution time and the α – λ plot as the performance metrics for comparing the effectiveness of the different resampling techniques. Our analysis shows that the residual systematic resampling algorithm is the most preferred approach considering the reasonable accuracy and short computational time.