Fatigue life prediction using hybrid prognosis for structural health monitoring

Rajesh Kumar Neerukatti, Kuang C. Liu, Yingtao Liu, Aditi Chattopadhyay

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

A reliable prognostics framework is essential to address failure mode mitigation and life cycle cost of aerospace systems. Metallic aircraft components are subject to a variety of in-service loading conditions and prediction of fatigue life remains a critical challenge. A hybrid prognostic model, which can accurately predict the crack growth regime and the residual useful life estimate (RULE) of aluminum components, is developed to address this issue. This model uses an integrated technique; coupling physics based approach with a data-driven approach. Different types of loading such as constant amplitude, random and overload are considered and the developed methodology is validated with the experimental data available in the literature. The results indicate that fusing the measured data and physics based models improves the accuracy of prediction compared to a pure data-driven or physics based approach.

Original languageEnglish (US)
Title of host publicationAIAA Infotech at Aerospace Conference and Exhibit 2012
StatePublished - Dec 1 2012
EventAIAA Infotech at Aerospace Conference and Exhibit 2012 - Garden Grove, CA, United States
Duration: Jun 19 2012Jun 21 2012

Publication series

NameAIAA Infotech at Aerospace Conference and Exhibit 2012

Other

OtherAIAA Infotech at Aerospace Conference and Exhibit 2012
CountryUnited States
CityGarden Grove, CA
Period6/19/126/21/12

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Neerukatti, R. K., Liu, K. C., Liu, Y., & Chattopadhyay, A. (2012). Fatigue life prediction using hybrid prognosis for structural health monitoring. In AIAA Infotech at Aerospace Conference and Exhibit 2012 (AIAA Infotech at Aerospace Conference and Exhibit 2012).