Minimizing Fatigue Damage in Aircraft Structures

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 aircra...

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Vydané v:IEEE intelligent systems Ročník 31; číslo 4; s. 22 - 29
Hlavní autori: Ruotsalainen, Marja, Jylha, Juha, Visa, Ari
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: IEEE 01.07.2016
Predmet:
Aerospace electronics
Aging aircraft
Aircraft
Aircraft components
applications and expert knowledge-intensive systems
Artificial intelligence
computing methodologies
decision support
Economics
engineering
evolutionary computing and genetic algorithms
Fatigue failure
Genetic algorithms
intelligent systems
machine learning
Military aircraft
Monitoring
pattern recognition
Structural engineering
Structural health monitoring
Wireless sensor networks
ISSN:1541-1672, 1941-1294
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Abstract 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.
AbstractList 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.
Author Visa, Ari
Ruotsalainen, Marja
Jylha, Juha
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10.1007/978-94-007-1664-3_36
10.1017/S0001924000004474
10.1109/CSIE.2009.958
10.1002/9780470061626
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10.1109/SECon.2012.6197066
10.1016/j.jsv.2007.07.035
10.1243/0954410971532479
10.1109/CEC.2013.6557964
10.1109/TASSP.1978.1163055
10.1007/BF00116251
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SubjectTerms Aerospace electronics
Aging aircraft
Aircraft
Aircraft components
applications and expert knowledge-intensive systems
Artificial intelligence
computing methodologies
decision support
Economics
engineering
evolutionary computing and genetic algorithms
Fatigue failure
Genetic algorithms
intelligent systems
machine learning
Military aircraft
Monitoring
pattern recognition
Structural engineering
Structural health monitoring
Wireless sensor networks
Title Minimizing Fatigue Damage in Aircraft Structures
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Volume 31
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