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Minimizing Fatigue Damage in Aircraft Structures

Research output: Contribution to journalArticleScientificpeer-review


Original languageEnglish
Pages (from-to) 22-29
Number of pages8
JournalIEEE Intelligent Systems
Issue number4
Publication statusPublished - 2016
Publication typeA1 Journal article-refereed


Aircraft structural health monitoring (SHM) refers to a process in which sensors assess the current (and predict the future) state of a structure in terms of its aging and deterioration to assure users or operators of its safety and performance. In addition to preventing failures, SHM extends aircraft life cycles. Consequently, adopting SHM is strongly motivated not only by flight safety but also by economic considerations. This article focuses on the optimization of aircraft usage as a new aspect of SHM and discusses a knowledge discovery approach based on dynamic time warping and genetic programming. In addition, it points out some of the challenges faced in applying artificial intelligence to aircraft SHM. This novel work reveals that AI provides a means to gain valuable knowledge for decision making on cost-efficient future usage of an aircraft fleet.


  • artificial intelligence, intelligent systems, machine learning, pattern recognition, decision support, evolutionary computation, engineering

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