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

By Adrian Cubillo, S. Perinpanayagam, M. Esperon-Miguez. 2016

255 citations recorded by the source corpus.

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.

Subfields: Control and Systems Engineering, Mechanical Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/69b6fd39a803cbe1d9cc689e2938940a4323ad83

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Naipeng Li
2. Yaguo Lei
3. Ningbo Li
4. Liang Guo
5. Tao Yan
6. Biao Wang
7. Jing Lin
8. Paulo da Costa

In Mechanical Engineering

1. Naipeng Li
2. Yaguo Lei
3. Jing Lin
4. Paulo da Costa
5. Uzay Kaymak
6. Tao Yan
7. Liang Guo
8. Ningbo Li

In Mechanics of Materials

1. Yaguo Lei
2. Naipeng Li
3. Ningbo Li
4. Liang Guo
5. Tao Yan
6. Biao Wang
7. Jing Lin
8. Shen Zhang

Citations in the corpus

• Remaining Useful Lifetime Prediction via Deep Domain Adaptation (2019, background): “Physics-based approaches build mathematical models that describe the degradation processes of the failure mechanisms [4].”
• A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings (2020, methodology): “Moreover, a model-based approach is often developed based on a specific case and also requires explicit prior knowledge or extensive empirical data to initialize model parameters [4].”
• Machinery health prognostics: A systematic review from data acquisition to RUL prediction (2018, methodology): “Physics model-based approaches Physics model-based approaches describe degradation processes of machinery through building mathematical models on the basis of the failure mechanisms or the first principle of damage [175].”