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Degradation modeling and RUL prediction using Wiener process subject to multiple change points and unit heterogeneity

机译：经受多个变化点和单位异质性的维纳过程退化建模和RUL预测

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摘要

Degradation modeling is critical for health condition monitoring and remaining useful life prediction (RUL). The prognostic accuracy highly depends on the capability of modeling the evolution of degradation signals. In many practical applications, however, the degradation signals show multiple phases, where the conventional degradation models are often inadequate. To better characterize the degradation signals of multiple-phase characteristics, we propose a multiple change-point Wiener process as a degradation model. To take into account the between-unit heterogeneity, a fully Bayesian approach is developed where all model parameters are assumed random. At the offline stage, an empirical two-stage process is proposed for model estimation, and a cross-validation approach is adopted for model selection. At the online stage, an exact recursive model updating algorithm is developed for online individual model estimation, and an effective Monte Carlo simulation approach is proposed for RUL prediction. The effectiveness of the proposed method is demonstrated through thorough simulation studies and real case study.
机译：退化模型对于健康状况监控和剩余使用寿命预测（RUL）至关重要。预后准确性高度取决于对降解信号的演变进行建模的能力。然而，在许多实际应用中，退化信号显示出多个相位，而传统的退化模型通常不足。为了更好地表征多相特性的退化信号，我们提出了多变化点维纳过程作为退化模型。考虑到单元之间的异质性，开发了一种完全贝叶斯方法，其中所有模型参数均假定为随机。在离线阶段，提出了一个经验的两阶段过程进行模型估计，并采用交叉验证的方法进行模型选择。在在线阶段，开发了一种精确的递归模型更新算法，用于在线个体模型估计，并提出了一种有效的蒙特卡洛仿真方法来进行RUL预测。通过全面的仿真研究和实际案例研究证明了该方法的有效性。

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