Rare event anticipation and degradation trending for aircraft predictive maintenance

S. Alestra, C. Bordry, C. Brand, E. Burnaev, P. Erofeev, A. Papanov, C. Silveira-Freixo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)

Abstract

In this paper we examine problem of predictive maintenance in complex technical systems. We propose two approaches for anticipation of rare events (typically faults): 1) degradation detection and trending, and 2) failure discrimination based on classification techniques. The applicability of the approaches is illustrated on the real-world test cases from aircraft operations based on the data granted by AIRBUS.

Original languageEnglish
Title of host publication11th World Congress on Computational Mechanics, WCCM 2014, 5th European Conference on Computational Mechanics, ECCM 2014 and 6th European Conference on Computational Fluid Dynamics, ECFD 2014
EditorsAntonio Huerta, Eugenio Onate, Xavier Oliver
PublisherInternational Center for Numerical Methods in Engineering
Pages6571-6582
Number of pages12
ISBN (Electronic)9788494284472
Publication statusPublished - 1 Jul 2014
Externally publishedYes
EventJoint 11th World Congress on Computational Mechanics, WCCM 2014, the 5th European Conference on Computational Mechanics, ECCM 2014 and the 6th European Conference on Computational Fluid Dynamics, ECFD 2014 - Barcelona, Spain
Duration: 20 Jul 201425 Jul 2014

Publication series

Name11th World Congress on Computational Mechanics, WCCM 2014, 5th European Conference on Computational Mechanics, ECCM 2014 and 6th European Conference on Computational Fluid Dynamics, ECFD 2014

Conference

ConferenceJoint 11th World Congress on Computational Mechanics, WCCM 2014, the 5th European Conference on Computational Mechanics, ECCM 2014 and the 6th European Conference on Computational Fluid Dynamics, ECFD 2014
Country/TerritorySpain
CityBarcelona
Period20/07/1425/07/14

Keywords

  • Aircraft health management
  • Data mining
  • Degradation trending
  • Predictive maintenance
  • Rare event anticipation

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