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

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

Lee, J. (TU Delft Air Transport & Operations)
Mitici, M.A. (Universiteit Utrecht)
Blom, H.A.P. (TU Delft Air Transport & Operations)
Bieber, Pierre (Office National d'Etudes et de Recherches Aerospatiales)
Freeman, Floris (KLM Royal Dutch Airlines)

2023

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.

agent-based modeling
brainstorming
predictive maintenance
aircraft maintenance
airworthiness

http://resolver.tudelft.nl/uuid:3fe02b0e-b40c-4b23-8773-ff67e5a6f65c

https://doi.org/10.3390/aerospace10020186

2226-4310

Aerospace, 10 (2)

Institutional Repository

journal article

© 2023 J. Lee, M.A. Mitici, H.A.P. Blom, Pierre Bieber, Floris Freeman