Predictive Maintenance of Aircraft Components Based on Sensor Data-Driven Approach: A Review

Abstract

Abstract: Predictive maintenance has gained significant attention in the aviation industry as a proactive strategy for enhancing aircraft safety, reducing downtime, and optimizing maintenance costs. Ensuring the reliability and efficiency of aircraft components has consistently been a significant focus in the aviation industry. Accurately anticipating possible malfunctions can significantly improve the dependability of these components and system fault detection and prediction in the aircraft industry play a critical role in preventing failures, minimizing maintenance expenses, and maximizing fleet availability. Unforeseen aircraft maintenance can cause flight cancellations or delays when spare parts are not readily available at the location of the failure. This leads to undesired downtime, thereby increasing operational costs for airlines. By employing predictive modelling, airlines can reduce unscheduled maintenance activities, resulting in cost savings and improved fleet availability. Implementing health monitoring and predictive maintenance practices for aircraft can also minimize unplanned groundings by implementing more systematic maintenance intervals, thereby avoiding situations where an aircraft is grounded (known as "aircraft on ground" or AOG) and the subsequent operational disruptions. This survey paper provides a comprehensive review of the state – of – the – art deep learning techniques employed in the field of predictive maintenance for aircraft components