Building degradation index with variable selection for multivariate sensory data
Building degradation index with variable selection for multivariate sensory data
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DOI：
10.1016/j.ress.2022.108704
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
Yueyao Wang;I-Chen Lee;Yili Hong;Xinwei Deng
中科院分区：
文献类型：
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作者：
Yueyao Wang;I-Chen Lee;Yili Hong;Xinwei Deng
退化数据的建模和分析一直是可靠性工程中用于可靠性评估和系统健康管理的一个活跃的研究领域。随着传感器技术的进步，通常收集多变量传感数据用于潜在的降解过程。然而，大多数现有的退化建模的研究需要一个单变量退化指数提供。因此，构建多元感官数据的退化指数是退化建模的基本步骤。在本文中，我们提出了一种新的多变量感官数据与删失的退化指数的建设方法。该方法基于一个具有变量选择的加性非线性模型，能够处理删失数据，并能自动选择信息丰富的传感器信号用于劣化指标。提出了一种自适应群惩罚似然估计方法。我们表明，所提出的方法优于现有的方法，通过模拟研究和分析的NASA喷气发动机传感器数据。
The modeling and analysis of degradation data have been an active research area in reliability engineering for reliability assessment and system health management. As the sensor technology advances, multivariate sensory data are commonly collected for the underlying degradation process. However, most existing research on degradation modeling requires a univariate degradation index to be provided. Thus, constructing a degradation index for multivariate sensory data is a fundamental step in degradation modeling. In this paper, we propose a novel degradation index building method for multivariate sensory data with censoring. Based on an additive nonlinear model with variable selection, the proposed method can handle censored data, and can automatically select the informative sensor signals to be used in the degradation index. The penalized likelihood method with adaptive group penalty is developed for parameter estimation. We demonstrate that the proposed method outperforms existing methods via both simulation studies and analyses of the NASA jet engine sensor data.