Multiple-Change-Point Modeling and Exact Bayesian Inference of Degradation Signal for Prognostic Improvement
Multiple-Change-Point Modeling and Exact Bayesian Inference of Degradation Signal for Prognostic Improvement
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DOI：
10.1109/tase.2018.2844204
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发表时间：
2019-04
影响因子：
5.6
通讯作者：
Yuxin Wen;Jianguo Wu;Qiang Zhou;T. Tseng
中科院分区：
文献类型：
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作者：
Yuxin Wen;Jianguo Wu;Qiang Zhou;T. Tseng
预测在现代工程系统智能维修决策中发挥着越来越重要的作用。在基于参数回归的方法中，在许多应用中，参数模型往往过于严格，无法对退化信号进行建模。在本文中，我们提出了一个贝叶斯多变化点（CP）建模框架，以更好地捕捉退化路径并改善预测。在离线建模阶段，提出了一种新的随机过程来建模CPs和位置的联合先验。所有的超参数都是通过一个经验的两阶段过程来估计的。在在线监测和剩余使用寿命（RUL）预测阶段，提出了一种递归更新算法，依次精确计算后验分布和RUL预测。为了控制计算量，在模型在线更新中提出了固定支持大小策略，在RUL预测中提出了部分蒙特卡罗策略。通过仿真和实际案例分析，验证了该方法的有效性和优越性。从业人员注意：退化信号已广泛用于确定组件或系统的当前健康状况和估计剩余使用寿命（RUL）。现有的预测方法大多采用参数回归模型来描述退化信号的演化路径，用于RUL预测。这些模型的常见函数形式包括简单的线性函数、二次函数和指数函数。然而，在许多应用中，退化信号呈现多段特征，现有的参数形式不足以捕捉退化趋势。针对这一问题，本文提出了一种多变化点（CP）建模方法，该方法将退化信号通过CP划分为几个连续的片段，每个片段由一个唯一的参数模型建模。为了捕捉不同单元之间的异质性，假设所有参数，包括CPs的数量和位置以及每个片段的模型参数都是遵循一定分布的随机变量。然后，我们开发了一种利用历史数据估计这些分布的统计方法。在在线监测阶段，我们开发了一种创新的更新算法，精确计算出最新CP、当前段和当前段模型参数的后验分布的封闭形式。我们还推导了RUL分布估计的封闭形式。随后，提出了几种有效的近似策略来减少计算量。仿真研究和实际案例研究表明，该方法在处理多段特征退化信号方面具有比现有方法更好的性能。
Prognostics play an increasingly important role in modern engineering systems for smart maintenance decision-making. In parametric regression-based approaches, the parametric models are often too rigid to model degradation signals in many applications. In this paper, we propose a Bayesian multiple-change-point (CP) modeling framework to better capture the degradation path and improve the prognostics. At the offline modeling stage, a novel stochastic process is proposed to model the joint prior of CPs and positions. All hyperparameters are estimated through an empirical two-stage process. At the online monitoring and remaining useful life (RUL) prediction stage, a recursive updating algorithm is developed to exactly calculate the posterior distribution and RUL prediction sequentially. To control the computational cost, a fixed-support-size strategy in the online model updating and a partial Monte Carlo strategy in the RUL prediction are proposed. The effectiveness and advantages of the proposed method are demonstrated through thorough simulation and real case studies. Note to Practitioners—Degradation signals have been widely used in determining the current health condition and estimate the remaining useful life (RUL) of a component or a system. Most of the existing prognostics utilize a parametric regression model to describe the evolution path of degradation signals for RUL prediction. The common functional forms of these models include simple linear, quadratic, and exponential functions. However, in many applications, the degradation signals show multiple-segment characteristics and the existing parametric forms are inadequate to capture the degradation trend. Motivated by such issue, this paper presents a multiple-change-point (CP) modeling approach, where the degradation signal is divided into several consecutive segments by CPs, and each segment is modeled by a unique parametric model. To capture the heterogeneity across different units, all the parameters, including the number and locations of CPs and model parameters of each segment, are assumed to be random variables following certain distributions. Then, we develop a statistical method to estimate these distributions using historical data. At the online monitoring stage, we develop an innovative updating algorithm to exactly calculate the closed forms of the posterior distributions of the latest CP, the current segment, and model parameters of the current segment. We also derive a closed form for the RUL distribution estimation. Later, several efficient approximation strategies are proposed to reduce the computational burden. Simulation studies and real case studies have shown that the proposed methodology has much better performance than those of existing approaches in handling degradation signals of multiple-segment characteristics.