Condition-Based Maintenance scheduling of an aircraft fleet under partial observability

Condition-Based Maintenance scheduling of an aircraft fleet under partial observability: A Deep Reinforcement Learning approach

Tseremoglou, I. (TU Delft Air Transport & Operations)
Santos, Bruno F. (TU Delft Air Transport & Operations)

2024

In the Condition-Based Maintenance (CBM) context, the definition of optimal maintenance plans for an aircraft fleet depends on an efficient integration of : (i) the probabilistic predictions of the health condition of the components and (ii) the stochastic arrival of the corrective maintenance tasks, together with consideration of the preventive maintenance tasks as defined in the Maintenance Planning Document (MPD). To this end, in this paper, we present a two-stage dynamic scheduling framework to solve the aircraft fleet maintenance scheduling problem under a CBM strategy in a disruptive environment. In the first stage of the framework, we address the uncertainty in the predicted health state of the monitored components by planning the optimal maintenance policy based upon the belief state-space of the health of the components. The decision-making process is formulated as a Partially Observable Markov Decision Process (POMDP) and is solved using the Partially Observable Monte Carlo Planning (POMCP) algorithm, considering the aircraft maintenance scheduling problem requirements. In the second stage, a Deep Q-Network (DQN) is developed, that integrates the defined maintenance policy of the monitored components within the scheduling of the aircraft fleet's preventive and corrective maintenance tasks. Our model, through a rolling horizon approach, continuously creates and adjusts the maintenance schedule, reacting to new updated task information, where the availability of maintenance resources constraints the execution of each task. The proposed framework was tested on a case study from a large airline and the performance was evaluated against the current state practice of the airline. The results show that our model can schedule 96.4% of monitored components on-time. As a consequence of this, a 46.2% maintenance cost reduction is achieved for the considered monitored components relative to a corrective maintenance approach.

(POMDP)
Condition-Based Maintenance (CBM)
Deep Reinforcement Learning
Partially Observable Markov Decision Process
Partially Observable Monte–Carlo Planning (POMCP)
Planning under uncertainty
Prognostics

http://resolver.tudelft.nl/uuid:e7aa213f-4a07-4c77-a8a7-cadbed12aca9

https://doi.org/10.1016/j.ress.2023.109582

0951-8320

Reliability Engineering & System Safety, 241

Institutional Repository

journal article

© 2024 I. Tseremoglou, Bruno F. Santos