期刊
IEEE TRANSACTIONS ON RELIABILITY
卷 67, 期 1, 页码 41-52出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TR.2017.2717190
关键词
Condition monitoring (CM); external factors; multivariate Gaussian process (MGP); remaining useful life (RUL) prediction
类别
资金
- National Science Foundation [1335129, 1343969]
The use of condition monitoring (CM) signals to predict the remaining useful life of in-service units plays a critical role in reliability engineering. Many models assume that CM signals behave under similar external conditions or that external factors have no effect on the evolution of these signals. These assumptions might not hold in real-life applications. In this paper, we propose a nonparametric framework for modeling the evolution of CM signals under different external factors. The unique feature of our model is that it does not assume any functional form for CM signals and is able to incorporate the effect of external factors through a reparametrization technique called hypersphere decomposition. Through extensive numerical studies and a case study on automotive lead-acid batteries, we demonstrate the advantageous features of our proposed method specifically when the evolution of CM signals is impacted by external factors.
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