Minimizing Fatigue Damage in Aircraft Structures

Marja Ruotsalainen; Juha Jylhä; Ari Visa

doi:10.1109/MIS.2016.23

Minimizing Fatigue Damage in Aircraft Structures

Marja Ruotsalainen, Juha Jylhä, Ari Visa

Research output: Contribution to journal › Article › Scientific › peer-review

6 Citations (Scopus)

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.

Original language	English
Pages (from-to)	22-29
Number of pages	8
Journal	IEEE Intelligent Systems
Volume	31
Issue number	4
DOIs	https://doi.org/10.1109/MIS.2016.23
Publication status	Published - 2016
Publication type	A1 Journal article-refereed

Keywords

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

Publication forum classification

Publication forum level 2

ASJC Scopus subject areas

Artificial Intelligence
Aerospace Engineering

Access to Document

10.1109/MIS.2016.23

6 Citations
5 Conference contribution

Reasoning Flight Parameter Based Rules: Why Do Nominally Similar Flight Maneuvers Cause Diverse Fatigue Damage
Ruotsalainen, M., Jylhä, J., Salonen, T., Janhunen, H. & Visa, A., 2013, 27th ICAF Symposium, June 5-7, 2013, Jerusalem, Israel. p. 1-14 14 p. (International Committee on Aeronautical Fatigue and Structural Integrity Symposium).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Reasoning Logical Rules from Multisensor Data for Lifecycle Management of Aircraft Structures
Ruotsalainen, M., Jylhä, J. & Visa, A., 2013, 2013 IEEE Congress on Evolutionary Computation, June 20-23, Cancun, Mexico. Cancun, Mexico: IEEE, p. 3221-3227 7 p. (IEEE Congress on Evolutionary Computation).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

4 Citations (Scopus)
Link between Flight Maneuvers and Fatigue
Jylhä, J., Ruotsalainen, M., Salonen, T., Janhunen, H., Venäläinen, I., Siljander, A. & Visa, A., 2011, ICAF 2011 Structural Integrity: Influence of Efficiency and Green Imperatives, June 1-3, Montreal, Canada. Komorowski, J. (ed.). Berlin: Springer, p. 453-463 (ICAF Structural Integrity: Influence of Efficiency and Green Imperatives Conference; vol. 5).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

4 Citations (Scopus)

Cite this

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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.",

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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

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JO - IEEE Intelligent Systems

JF - IEEE Intelligent Systems

IS - 4

ER -

Minimizing Fatigue Damage in Aircraft Structures

Abstract

Keywords

Publication forum classification

ASJC Scopus subject areas

Access to Document

Fingerprint

Research output

Reasoning Flight Parameter Based Rules: Why Do Nominally Similar Flight Maneuvers Cause Diverse Fatigue Damage

Reasoning Logical Rules from Multisensor Data for Lifecycle Management of Aircraft Structures

Link between Flight Maneuvers and Fatigue

Cite this