A Hybrid TLBO-Particle Filter Algorithm Applied to Remaining Useful Life Prediction in the Presence of Multiple Degradation Factors

Abstract

One of the end goals of a Prognostic and Health Monitoring (PHM) algorithm is to provide accurate Remaining Useful Life (RUL) predictions for the monitored component or system. Most of the PHM algorithms found in the literature are based on the assumption that the degradation process is governed by only one degradation factor. However, some components and systems may be subject to multiple degradation factors. In this paper, we propose a hybrid algorithm that incorporates a Teaching-Learning Based optimization (TLBO) step into a Particle Filter (PF) framework. PF is an algorithm that can handle multiple degradation factors. However, it has some drawbacks such as sample degeneracy and sample impoverishment. The hybrid TLBO-PF algorithm proposed in this paper improves the performance of the standard PF algorithm by reducing the effects of sample degeneracy and sample impoverishment. A case study is presented to evaluate the performance of the proposed algorithm for estimating the degradation factors and predicting the RUL of a Lithium-ion battery, which is affected by two degradation factors. The results show that the proposed algorithm presented a better performance for both the tasks (degradation factor estimation and RUL prediction) when compared with the standard Particle Filter algorithm.