Adaptive Wiener process–based remaining useful life prediction method considering multi-source variability
Year: 2024
Type: article
Abstract: Existing remaining useful life (RUL) prediction methods considering multi-source variability were not applicable to the situation that the uneven measurement interval distribution and inconsistent measurement frequency of degrading equipment. This type of method also has ignored the variability of adaptive drift in the future degradation process. In view of this, based on adaptive Wiener process, the paper proposes a new nonlinear degradation method of the RUL prediction. Firstly, adopting the adaptive Wiener process, we have constructed the nonlinear degradation model with multi-source variability, which randomness of the parameters in the nonlinear function. Secondly, the real-time estimation of multiple hidden states can be realized by the particle filter algorithm. It has derived the RUL distribution in the sense of first hitting time. Using monitoring data of degrading equipment, the adaptive update of model parameters was implemented by expectation maximization algorithm. Finally, the effectiveness and superiority of the proposed model are validated through numerical simulation and lithium-ion battery experiments. The results show that it can effectively improve the prediction accuracy, which has potential application value.
Source: Heliyon
Authors: Jianfei Zheng, Qing Dong, Xuanjun Wang, Qingchao Zhang, Dangbo Du
Institution: PLA Rocket Force University of Engineering
Language: English
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,100
OpenAlex ID: W4401423402