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Minimizing Fatigue Damage in Aircraft Structures

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Minimizing Fatigue Damage in Aircraft Structures. / Ruotsalainen, Marja; Jylhä, Juha; Visa, Ari.

In: IEEE Intelligent Systems, Vol. 31, No. 4, 2016, p. 22-29.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

Ruotsalainen, M, Jylhä, J & Visa, A 2016, 'Minimizing Fatigue Damage in Aircraft Structures', IEEE Intelligent Systems, vol. 31, no. 4, pp. 22-29. https://doi.org/10.1109/MIS.2016.23

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Ruotsalainen, Marja ; Jylhä, Juha ; Visa, Ari. / Minimizing Fatigue Damage in Aircraft Structures. In: IEEE Intelligent Systems. 2016 ; Vol. 31, No. 4. pp. 22-29.

Bibtex - Download

@article{320bd0db8f0e4526a0582ca1a03750a0,
title = "Minimizing Fatigue Damage in Aircraft Structures",
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.",
keywords = "artificial intelligence, intelligent systems, machine learning, pattern recognition, decision support, evolutionary computation, engineering",
author = "Marja Ruotsalainen and Juha Jylh{\"a} and Ari Visa",
year = "2016",
doi = "10.1109/MIS.2016.23",
language = "English",
volume = "31",
pages = "22--29",
journal = "IEEE Intelligent Systems",
issn = "1541-1672",
publisher = "Institute of Electrical and Electronics Engineers",
number = "4",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Minimizing Fatigue Damage in Aircraft Structures

AU - Ruotsalainen, Marja

AU - Jylhä, Juha

AU - Visa, Ari

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

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

U2 - 10.1109/MIS.2016.23

DO - 10.1109/MIS.2016.23

M3 - Article

VL - 31

SP - 22

EP - 29

JO - IEEE Intelligent Systems

JF - IEEE Intelligent Systems

SN - 1541-1672

IS - 4

ER -