Description
Because metallic aircraft components are subject to a variety of in-service loading conditions, predicting their fatigue life has become a critical challenge. To address the failure mode mitigation of aircraft components and at the same time reduce the life-cycle costs of aerospace systems, a reliable prognostics framework is essential. In this paper, a hybrid prognosis model that accurately predicts the crack growth regime and the residual-useful-life estimate of aluminum components is developed. The methodology integrates physics-based modeling with a data-driven approach. Different types of loading conditions such as constant amplitude, random, and overload are investigated. The developed methodology is validated on an Al 2024-T351 lug joint under fatigue loading conditions. The results indicate that fusing the measured data and physics-based models improves the accuracy of prediction compared to a purely data-driven or physics-based approach.
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Details
Title
- Fatigue Life Prediction Using Hybrid Prognosis for Structural Health Monitoring
Contributors
- Neerukatti, Rajesh Kumar (Author)
- Liu, Kuang C. (Author)
- Kovvali, Narayan (Author)
- Chattopadhyay, Aditi (Author)
- Ira A. Fulton Schools of Engineering (Contributor)
- School for the Engineering of Matter, Transport and Energy (Contributor)
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2014-04-01
Resource Type
Collections this item is in
Identifier
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Digital object identifier: 10.2514/1.I010094
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Identifier TypeInternational standard serial numberIdentifier Value2327-3097
Note
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This is the authors' final accepted manuscript. The final version as published is available at http://dx.doi.org/10.2514/1.I010094
Citation and reuse
Cite this item
This is a suggested citation. Consult the appropriate style guide for specific citation guidelines.
Neerukatti, Rajesh Kumar, Liu, Kuang C., Kovvali, Narayan, & Chattopadhyay, Aditi (2014). Fatigue Life Prediction Using Hybrid Prognosis for Structural Health Monitoring. JOURNAL OF AEROSPACE INFORMATION SYSTEMS, 11(4), 211-231. http://dx.doi.org/10.2514/1.I010094