Dynamic production capacity assessment of aircraft overhaul shop based on digital twin

Abstract

Aircraft overhaul is of the highest level in aircraft MRO (Maintenance, Repair, Overhaul), which is an important and necessary activity for ensuring safety and reliability of aircraft throughout its whole lifecycle. Production capacity of aircraft overhaul shop has huge strategic value and determines the cycle time and throughput of the overhauled aircraft. However, due to the inherent complexity and stochasticity, aircraft overhaul tasks are generally executed manually by skilled workers, whose stochastic task execution time (TET) makes it challenging to assess the production capacity of aircraft overhaul shop. Digital twin provides an up-to-date technical measure to simulate, evaluate, and predict the whole lifecycle process of its physical entity, which makes it possible to tackle the complexity and stochasticity of aircraft overhaul process. This paper proposes to apply digital twin to assess the production capacity of aircraft overhaul shop dynamically. Firstly, the aircraft overhaul process and its dynamic production capacity assessment are analyzed. A modeling framework of aircraft overhaul shop digital twin is proposed with a novel twin fusion mechanism. Then, an improved kernel density estimation (KDE) method and a quota hour density proportion (QHDP) method are proposed and verified to process stochastic TET for production capacity assessment. Finally, the digital twin of a typical aircraft avionics repair shop is constructed to assess its production capacity. Four assessment results are compared with real observations from the aircraft avionics repair shop, which verifies the feasibility and advantage of the proposed methodology. A bottleneck analysis is also conducted to optimize the production capacity, which provides managerial insights.