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Abstract:
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The modeling and analysis of degradation data have been an active research area in reliability and system health management. Most of the existing research on degradation modeling assumes that the degradation index is provided. However, there are situations that a degradation index is not available. For example, modern sensor technology allows one to collect multi-channel sensor data that are related to the underlying degradation process, which may not be sufficiently represented by any single channel. Thus, constructing a degradation index is a fundamental step in degradation modeling. In this paper, we develop a general approach for degradation index building based on an additive-nonlinear model with variable selection. The approach is more flexible than a linear combination of sensor signals, and it can automatically select the most informative variables to be used in the degradation index. Maximum likelihood estimation with adaptive group penalty is developed based on training dataset. We use extensive simulations to validate the performance of the developed method. The NASA jet engine sensor dataset is then used for illustration. The paper is concluded with some discussions.
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