A hybrid prognostic method for system degradation based on particle filter and relevance vector machine. (June 2019) - Chang, Yang; Fang, Huajing
Highlights: The proposed prognostic method can provide accurate and stable RUL prediction. The proposed prognostic method can construct a prediction interval to assess the prediction uncertainty. Four types of comparative experiments are performed to verify the wide applicability of the proposed method; Reliable prognostic result can be provided by proposed method to ensure the system reliability. Abstract: Prognostics of the remaining useful life has become a critical technique to ensure the reliability and safety of system, however, due to the uncertainty of system degradation, the prognostic result is usually not so satisfactory. To solve this problem, a hybrid prognostic scheme with the capability of uncertainty assessment is proposed in this paper, which combines particle filter (PF) and relevance vector machine (RVM). The prognostic result comprises a set of deterministic prediction values to represent the degradation process and a prediction interval to evaluate the prediction uncertainty. In order to examine the performance of the proposed hybrid method, four types of comparative experiments based on two types of lithium-ion battery datasets and two degradation models are performed. The experimental results show that the proposed hybrid scheme is a reliable prognostic method which can ensure the accuracy of the deterministic prediction result and provide precise assessment for the prediction uncertainty.