A physics-regularized data-driven approach for health prognostics of complex engineered systems with dependent health states
A physics-regularized data-driven approach for health prognostics of complex engineered systems with dependent health states
复制标题
一种物理正则化数据驱动方法，用于对具有相关健康状态的复杂工程系统进行健康预测
DOI：
10.1016/j.ress.2022.108677
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发表时间：
2022
影响因子：
8.1
通讯作者：
Ramin, Moghaddass
中科院分区：
文献类型：
--
作者：
Hajiha, Mohammadmahdi;Liu, Xiao;Lee, Young M.;Ramin, Moghaddass
传感技术的进步使得能够监测复杂工程系统的关键操作参数。然而，具有传感器测量值并不一定意味着已经观察到真实的系统健康状态，其通常是隐藏的并且需要从可观察的传感器信号来估计。针对具有多个隐藏和依赖健康状态的复杂工程系统，提出了一种物理正则化数据驱动的健康预测方法。该框架由数据层和物理层组成。数据层捕获隐藏的系统状态（如退化）的时间动态，而物理层通过系统工作原理和管理物理学在观察到的系统操作参数和系统健康状态之间施加规则化。所提出的方法解决了一些常见的挑战所产生的复杂工程系统的健康特性，包括工程领域的知识和传感器数据流的集成，隐藏的系统健康状态的估计从监测系统的运行参数，和统计依赖性的时间动态多个系统状态变量。基于一个真实的数据集的案例研究来说明所提出的物理统计方法。它表明，数据驱动的系统物理的可解释性可以显着加强，如果传感器数据和系统物理之间建立了坚实的连接。
Advances in sensing technology enable the monitoring of critical operating parameters of complex engineering systems. However, having sensor measurements does not necessarily imply that one has observed the true system health states, which are often hidden and need to be estimated from observable sensor signals. This paper proposes a physics-regularized data-driven approach for the health prognostics of complex engineered systems with multiple hidden and dependent health states. The framework consists of a data layer and a physics layer. The data layer captures the statistically-correlated temporal dynamics of hidden system states (such as degradation), while the physics layer imposes regularizations among observed system operating parameters and system health states through system working principles and governing physics. The proposed approach addresses some common challenges arising from the health prognostics of complex engineered systems, including the integration of engineering domain knowledge and sensor data streams, the estimation of hidden system health states from monitored system operation parameters, and the statistical dependency among the temporal dynamics of multiple system state variables. A case study based on a real dataset is presented to illustrate the proposed physical–statistical approach. It is shown that the interpretability of data-driven system prognostics can be significantly strengthened if a solid connection is established between sensor data and system physics.