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Genetic algorithms and decision trees for condition monitoring and prognosis of A320 aircraft air conditioning
Department of Automotive and Aeronautical Engineering, HAW Hamburg.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-4107-0991
Hamburg University of Applied Sciences, Aero - Aircraft Design and Systems Group.
2017 (English)In: Insight (Northampton), ISSN 1354-2575, E-ISSN 1754-4904, Vol. 59, no 8, 424-433 p.Article in journal (Refereed) Published
Abstract [en]

Unscheduled maintenance is a large cost driver for airlines, but condition monitoring and prognosis can reduce the number of unscheduled maintenance actions. This paper discusses how condition monitoring can be introduced into most systems by adopting a data-driven approach and using existing data sources. The goal is to forecast the remaining useful life (RUL) of a system based on various sensor inputs. Decision trees are used to learn the characteristics of a system. The data for the decision tree training and classification are processed by a generic parametric signal analysis. To obtain the best classification results for the decision tree, the parameters are optimised by a genetic algorithm. A forest of three different decision trees with different signal analysis parameters is used as a classifier. The proposed method is validated with data from an A320 aircraft from Etihad Airways. Validation shows that condition monitoring can classify the sample data into ten predetermined categories, representing the total useful life (TUL) in 10% steps. This is used to predict the RUL. There are 350 false classifications out of 850 samples. Noise reduction reduces the outliers to nearly zero, making it possible to correctly predict condition. It is also possible to use the classification output to detect a maintenance action in the validation data.

Place, publisher, year, edition, pages
British Institute of Non-Destructive Testing , 2017. Vol. 59, no 8, 424-433 p.
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-65081DOI: 10.1784/insi.2017.59.8.424ISI: 000408276100008Scopus ID: 2-s2.0-85026321620OAI: oai:DiVA.org:ltu-65081DiVA: diva2:1131611
Note

Validerad; 2017; Nivå 2; 2017-08-15 (andbra)

Available from: 2017-08-15 Created: 2017-08-15 Last updated: 2017-11-24Bibliographically approved

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CiteExportLink to record
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  • apa
  • harvard1
  • ieee
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  • vancouver
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  • de-DE
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