Empowering Predictive Maintenance: A Hybrid Method to Diagnose Abnormal Situations

Dennys Wallace Duncan Imbassahy, Henrique Costa Marques*, Guilherme Conceição Rocha , Alberto Martinetti

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
6 Downloads (Pure)

Abstract

Aerospace systems are composed of hundreds or thousands of components and complex subsystems which need an appropriate health monitoring capability to enable safe operation in various conditions. In terms of monitoring systems, it is possible to find a considerable number of state-of-the-art works in the literature related to ad-hoc solutions. Still, it is challenging to reuse them even with subtle differences in analogous subsystems or components. This paper proposes the Generic Anomaly Detection Hybridization Algorithm (GADHA) aiming to build a more reusable algorithm to support anomaly detection. The solution consists of analyzing different supervised machine learning classification algorithms combined in ensemble techniques, with a physical model when available, and two levels of a decision to estimate the current state of the monitored system. Finally, the proposed algorithm assures at least equal, or, more typically, better, overall accuracy in fault detection and isolation than the application of such algorithms alone, through few adaptations.
Original languageEnglish
Article number6929
Pages (from-to)1-27
Number of pages27
JournalApplied Sciences (Switzerland)
Volume10
Issue number19
DOIs
Publication statusPublished - 3 Oct 2020

Keywords

  • Predictive maintenance
  • anomaly detection
  • diagnose
  • hybrid method
  • fault classification

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