Stochastic prognostics under multiple time-varying environmental factors
Stochastic prognostics under multiple time-varying environmental factors
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DOI：
10.1016/j.ress.2021.107877
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Salman Jahani;Shiyu Zhou;D. Veeramani
中科院分区：
文献类型：
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作者：
Salman Jahani;Shiyu Zhou;D. Veeramani
传统上，现场部件剩余使用寿命的预测依赖于与系统物理退化相关的状态监测信号。许多模型假定状态监测信号在相似的环境条件(如压力、温度、工作量和相对湿度)下表现，或者这些条件对退化过程没有影响。在本文中，我们提出了一个具有应力依赖漂移的布朗运动过程来模拟多个时变的环境协变量。在此基础上，提出了一种基于惩罚样条法的半参数回归方法来模拟环境协变量-漂移关系。我们的方法的独特之处在于它不假定退化过程漂移的函数形式，并且模拟了多个环境协变量对退化过程的影响。此外，该模型还与现场单元及其环境条件的现场退化测量相结合，通过贝叶斯更新方案预测单元的剩余使用寿命。通过基于数值研究的分析和使用从连接的油炸锅收集的真实世界的油炸油降解数据的案例研究，对所提出的框架的性能进行了调查和基准测试。
Prediction of the remaining useful life of in-field components, traditionally, relies on condition monitoring signals which are correlated with the physical degradation of the system. Many models assume that condition monitoring signals behave under similar environmental conditions (e.g. pressure, temperature, workload and relative humidity) or these conditions have no effect on degradation process. In this paper, we propose a Brownian motion process with a stress-dependent drift to model multiple time-varying environmental covariates. A semiparametric regression approach utilizing penalized splines is, further, proposed to model the environmental covariates-drift relationship. The unique feature of our approach is that it does not assume a functional form for the degradation process drift and models multiple environmental covariates’ effect on the degradation process. Moreover, the model is combined with in situ degradation measurements of the in-field unit and its environmental conditions to predict the unit’s remaining useful life through a Bayesian updating scheme. The performance of the proposed framework is investigated and benchmarked through analysis based on numerical studies and a case study using real-world data of frying oil degradation collected from connected fryers.