Evolving Fuzzy System Applied to Battery Charge Capacity Prediction for Fault Prognostics

Autor:	Camargos, Murilo Osorio, Bessa, Iury, Junior, Luiz A. Q. Cordovil, Coutinho, Pedro Henrique Silva, Leite, Daniel Furtado, Palhares, Reinaldo Martinez
Rok vydání:	2021
Předmět:	Electrical Engineering and Systems Science - Systems and Control Mathematics - Optimization and Control
Druh dokumentu:	Working Paper
Popis:	This paper addresses the use of data-driven evolving techniques applied to fault prognostics. In such problems, accurate predictions of multiple steps ahead are essential for the Remaining Useful Life (RUL) estimation of a given asset. The fault prognostics' solutions must be able to model the typical nonlinear behavior of the degradation processes of these assets, and be adaptable to each unit's particularities. In this context, the Evolving Fuzzy Systems (EFSs) are models capable of representing such behaviors, in addition of being able to deal with non-stationary behavior, also present in these problems. Moreover, a methodology to recursively track the model's estimation error is presented as a way to quantify uncertainties that are propagated in the long-term predictions. The well-established NASA's Li-ion batteries data set is used to evaluate the models. The experiments indicate that generic EFSs can take advantage of both historical and stream data to estimate the RUL and its uncertainty. Comment: 8 pages, 5 figures, IFSA-EUSFLAT 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2102.09521 Zobrazit plný text záznamu View this record from Arxiv