Remaining useful life prediction based on a multi-sensor data fusion model

Naipeng Li, Nagi Gebraeel, Yaguo Lei, Xiaolei Fang, Xiao Cai and Tao Yan

Reliability Engineering and System Safety, 2021, vol. 208, issue C

Abstract: With the rapid development of Industrial Internet of Things, more and more sensors have been used for condition monitoring and prognostics of industrial systems. Big data collected from sensor networks bring abundant information resources as well as technical challenges for remaining useful life (RUL) prediction. The major technical challenges include how to select informative sensors and fuse multi-sensor data to improve the prediction performance. To deal with the challenges, this paper proposes a RUL prediction method based on a multi-sensor data fusion model. In this method, the inherent degradation process of the system state is expressed using a state transition function following a Wiener process. Multi-sensor signals are explicated as various proxies of the inherent system degradation process using a multivariate measurement function. The system state is estimated by fusing multi-sensor signals using particle filtering. Informative sensors are selected by a prioritized sensor group selection algorithm. This algorithm first prioritizes sensors according to their individual performances in RUL prediction, and then selects an optimal sensor group based on their combined performances. The effectiveness of the proposed method is demonstrated using a simulation study and aircraft engine degradation data from NASA repository.

Keywords: Prognostic degradation modeling; Remaining useful life prediction; Big data; Multi-sensor fusion; State-space model (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (30)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:208:y:2021:i:c:s0951832020307493

DOI: 10.1016/j.ress.2020.107249

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