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系统级预测的挑战与机遇。

Challenges and Opportunities of System-Level Prognostics.

机构信息

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.

出版信息

Sensors (Basel). 2021 Nov 18;21(22):7655. doi: 10.3390/s21227655.

Abstract

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.

摘要

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。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f5fe/8625866/4d2a4fa81285/sensors-21-07655-g001.jpg

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