Analyzing Emerging Challenges for Data-Driven Predictive Aircraft Maintenance Using Agent-Based Modeling and Hazard Identification

J. Lee, M.A. Mitici, H.A.P. Blom, Pierre Bieber, Floris Freeman

Research output: Contribution to journalArticleScientificpeer-review

8 Citations (Scopus)
52 Downloads (Pure)

Abstract

The increasing use of on-board sensor monitoring and data-driven algorithms has stimulated the recent shift to data-driven predictive maintenance for aircraft. This paper discusses emerging challenges for data-driven predictive aircraft maintenance. We identify new hazards associated with the introduction of data-driven technologies into aircraft maintenance using a structured brainstorming conducted with a panel of maintenance experts. This brainstorming is facilitated by a prior modeling of the aircraft maintenance process as an agent-based model. As a result, we identify 20 hazards associated with data-driven predictive aircraft maintenance. We validate these hazards in the context of maintenance-related aircraft incidents that occurred between 2008 and 2013. Based on our findings, the main challenges identified for data-driven predictive maintenance are: (i) improving the reliability of the condition monitoring systems and diagnostics/prognostics algorithms, (ii) ensuring timely and accurate communication between the agents, and (iii) building the stakeholders’ trust in the new data-driven technologies.
Original languageEnglish
Article number186
Number of pages17
JournalAerospace — Open Access Aeronautics and Astronautics Journal
Volume10
Issue number2
DOIs
Publication statusPublished - 2023

Keywords

  • agent-based modeling
  • brainstorming
  • predictive maintenance
  • aircraft maintenance
  • airworthiness

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