Please use this identifier to cite or link to this item: https://doi.org/10.1109/TII.2017.2684821
Title: RUL Prediction of Deteriorating Products Using An Adaptive Wiener Process Model
Authors: ZHAI QINGQING 
YE ZHISHENG 
Keywords: Adaptive drift
Degradation modeling
Remaining useful life (RUL)
Wiener process
Issue Date: 21-Mar-2017
Publisher: IEEE Computer Society
Citation: ZHAI QINGQING, YE ZHISHENG (2017-03-21). RUL Prediction of Deteriorating Products Using An Adaptive Wiener Process Model. IEEE Transactions on Industrial Informatics 13 (6) : 2911-2921. ScholarBank@NUS Repository. https://doi.org/10.1109/TII.2017.2684821
Abstract: Degradation modeling plays an important role in system health diagnosis and remaining useful life (RUL) prediction. Recently, a class of Wiener process models with adaptive drift was proposed for degradation-based RUL prediction, which has been proven flexible and effective. However, the existing studies use an autoregressive model of order 1 for the adaptive drift, which can result in difficulties in both model estimation and RUL prediction. This paper proposes a new adaptive Wiener process model that utilizes a Brownian motion for the adaptive drift. The new model shares the flexibility of the existing models, but avoids the difficulties in model estimation and RUL prediction. A model estimation procedure based on maximum likelihood estimation is developed, and the RUL prediction based on the proposed model is formulated. The effectiveness of the model in RUL prediction is validated using simulation and through an application to the lithium-ion battery degradation data.
Source Title: IEEE Transactions on Industrial Informatics
URI: https://scholarbank.nus.edu.sg/handle/10635/168382
ISSN: 15513203
DOI: 10.1109/TII.2017.2684821
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