- author = "Marja Ruotsalainen and Juha Jylha and Ari Visa",
- title = "Minimizing Fatigue Damage in Aircraft Structures",
- journal = "IEEE Intelligent Systems",
- year = "2016",
- volume = "31",
- number = "4",
- pages = "22--29",
- month = jul,
- keywords = "genetic algorithms, genetic programming, Aircraft, Artificial intelligence, Fatigue, Libraries, Military aircraft, Monitoring, Optimisation",
-
DOI = "
doi:10.1109/MIS.2016.23",
- ISSN = "1541-1672",
- size = "8 pages",
- abstract = "Aircraft structural health monitoring (SHM) refers to a process in which sensors are employed to assess the current state and to predict the future state of the structure in terms of its ageing and deterioration. Besides preventing failures, SHM allows extending the aircraft life cycle. Consequently, adopting SHM is strongly motivated not only by flight safety but also by economic considerations. Aircraft are designed for a certain life time. If one is operated in a manner more aggressive than expected, its life time will be diminished. It has been difficult to monitor the pilot's role in aircraft structural deterioration. This article focuses on the optimisation of aircraft usage as a new aspect of SHM, discusses our knowledge discovery approach based on dynamic time warping and genetic programming, and points out some of the challenges faced in applying AI to aircraft SHM. The proposed approach provides means to gain valuable knowledge for decision making on cost-efficient future usage of an aircraft fleet.",
-
notes = "Tampere University of Technology, Tampere
Also known as \cite{7412616}",
