A review of physics-based models in prognostics: Application to gears and bearings of rotating machinery

预言 失效物理学 状态监测 工程类 鉴定(生物学) 状态维修 系统工程 风险分析(工程) 控制工程 可靠性工程 可靠性(半导体) 物理 电气工程 生物 医学 功率(物理) 量子力学 植物
作者
Adrian Cubillo,Suresh Perinpanayagam,Manuel Esperon-Miguez
出处
期刊:Advances in Mechanical Engineering [SAGE Publishing]
卷期号:8 (8) 被引量:239
标识
DOI:10.1177/1687814016664660
摘要

Health condition monitoring for rotating machinery has been developed for many years due to its potential to reduce the cost of the maintenance operations and increase availability. Covering aspects include sensors, signal processing, health assessment and decision-making. This article focuses on prognostics based on physics-based models. While the majority of the research in health condition monitoring focuses on data-driven techniques, physics-based techniques are particularly important if accuracy is a critical factor and testing is restricted. Moreover, the benefits of both approaches can be combined when data-driven and physics-based techniques are integrated. This article reviews the concept of physics-based models for prognostics. An overview of common failure modes of rotating machinery is provided along with the most relevant degradation mechanisms. The models available to represent these degradation mechanisms and their application for prognostics are discussed. Models that have not been applied to health condition monitoring, for example, wear due to metal–metal contact in hydrodynamic bearings, are also included due to its potential for health condition monitoring. The main contribution of this article is the identification of potential physics-based models for prognostics in rotating machinery.
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