Modular management and analysis of predictive aircraft maintenance using colored Petri net tools

The expenses of an airline can rise dramatically as a result of unscheduled maintenance. However, most studies have primarily concentrated on predictive maintenance and other advanced maintenance strategies to reduce costs and improve the safety and effectiveness of aeronautical operations. This paper proposes a formal framework for modeling the aircraft maintenance process that incorporates dynamic interactions between the various stakeholders by utilizing an agent-based system and colored Petri nets. Our current focus lies in integrating the newly developed resource management team agent into the maintenance process and modeling the interaction among the six agents: the task generating team, the task planning team, the mechanics team, the flight crew, the data management team (DM), and the resource management team. The aim of this research is to identify the importance of resource availability and to reduce the number of unscheduled maintenance tasks. To test the efficiency of our approach, we apply our framework for an aircraft’s auxiliary power unit and employ a predictive maintenance strategy based on the remaining useful life. We analyze the number of tasks for a fleet of 10 immobilized aircrafts. The results indicate that the number of immobilized aircraft and unfulfilled tasks can be decreased by using predictive maintenance aircraft strategies and taking the resource management team into account during the maintenance process. In addition to improving costs, predictive maintenance techniques can preserve the same degree of safety as conventional time-based maintenance techniques. Additionally, the foundation of our suggested framework is the modeling of predictive maintenance as an agent-based system that involves the interaction of the major agents in the maintenance process. This framework can be used to assess how effectively aircraft component maintenance is carried out.