期刊
RELIABILITY ENGINEERING & SYSTEM SAFETY
卷 186, 期 -, 页码 51-63出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.ress.2019.02.011
关键词
Prognostics; Particle filter; Relevance vector machine; Deterministic prediction; Prediction interval; Lithium-ion battery
资金
- National Natural Science Foundation of China [61473127]
- CALCE of University of Maryland
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.
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