Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL 32611, USA.
School of Aerospace and Mechanical Engineering, Korea Aerospace University, Goyang 10540, Korea.
Sensors (Basel). 2021 Nov 18;21(22):7655. doi: 10.3390/s21227655.
Prognostics and health management (PHM) has become an essential function for safe system operation and scheduling economic maintenance. To date, there has been much research and publications on component-level prognostics. In practice, however, most industrial systems consist of multiple components that are interlinked. This paper aims to provide a review of approaches for system-level prognostics. To achieve this goal, the approaches are grouped into four categories: health index-based, component RUL-based, influenced component-based, and multiple failure mode-based prognostics. Issues of each approach are presented in terms of the target systems and employed algorithms. Two examples of PHM datasets are used to demonstrate how the system-level prognostics should be conducted. Challenges for practical system-level prognostics are also addressed.
预测与健康管理（PHM）已经成为安全系统运行和经济维护调度的必要功能。迄今为止，已经有大量关于部件级预测的研究和出版物。然而，在实际中，大多数工业系统由多个相互关联的部件组成。本文旨在综述系统级预测的方法。为了实现这一目标，这些方法被分为四类：基于健康指数、基于部件 RUL、基于受影响部件和基于多种失效模式的预测。针对每种方法，从目标系统和所采用的算法两个方面介绍了存在的问题。使用两个 PHM 数据集的示例来说明如何进行系统级预测。还讨论了实际系统级预测面临的挑战。
