his.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Genetic algorithms and decision trees for condition monitoring and prognosis of A320 aircraft air conditioning
Hamburg Univ Appl Sci, Aeroaircraft Design & Syst Grp, Hamburg, Germany..
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Luleå Univ Technol, Div Operat & Maintenance Engn, Luleå, Sweden. (Produktion och Automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0002-4107-0991
Hamburg Univ Appl Sci, Aeroaircraft Design & Syst Grp, Hamburg, Germany.
2017 (English)In: International Journal of Condition Monitoring, 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
2017. Vol. 59, no 8, 424-433 p.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Production and Automation Engineering
Identifiers
URN: urn:nbn:se:his:diva-14118DOI: 10.1784/insi.2017.59.8.424ISI: 000408276100008Scopus ID: 2-s2.0-85026321620OAI: oai:DiVA.org:his-14118DiVA: diva2:1141442
Available from: 2017-09-14 Created: 2017-09-14 Last updated: 2017-09-14Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Galar, Diego
By organisation
School of Engineering ScienceThe Virtual Systems Research Centre
In the same journal
International Journal of Condition Monitoring
Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf