Uncertain Particle Filtering: A New Real-Time State Estimation Method for Failure Prognostics

Particle filtering (PF) has become a state-of-the-art method in predicting the future degradation trend of the target equipment based on its current state, with its advantage in sequentially processing the observed data for continual state estimation. The convergence speed is important in PF for real-time state estimation. However, the Bayesian theorem can only converge when sufficient observations are available, which does not always fulfill the requirement in time-varying scenarios with abrupt changes in health state. In this work, based on the newly proposed Uncertainty Theory, Uncertain Particle Filtering (UPF) is derived for the first time. The initialization, prediction, update, and resampling processes are explained in detail in the scope of Uncertainty Theory. The UPF method significantly improves the performance of traditional particle filters by enhancing the speed of convergence in dynamic parameter estimation. Resampling techniques are introduced to mitigate particle phagocytosis, thereby improving computational accuracy and efficiency. Two case studies, addressing the degradation of the capacitor in an enhanced electromagnetic railgun and the degradation of the battery, are conducted to verify the effectiveness of the proposed UPF method. The results show that the UPF method achieves a faster convergence speed compared to traditional approaches.