Aircraft Maintenance and Predictive System

This paper discusses the use of predictive maintenance systems throughout the aircraft lifecycle to apply machine-learning algorithms that can maximize maintenance and minimize cost and risk. Conventional TBM approaches often result in unwarranted down times and that also avoidable wear and tear of the parts, where-as CBM using predictive systems offers a data-centric approach to track the real time health of the systems. In this paper, multiple machine-learning models, such as Random Forest, LSTMs and GBM are explored for predicting failures and RUL of aircraft components. Leveraging data from the actual usage of the equipment, sensors in real-time, flight records, and maintenance logs, the system forecasts failures including maintenance alerts [17] on time, and helps schedule the maintenance services proactively. Results indicate that predictive models can generate substantial savings in maintenance costs, decreased downtime and reduced site inefficiency compared to current methods. Furthermore, the rendering of real-time data processing in the form of data visualization aides’ teams in decision making, providing a full view of the health of the system for maintenance. The paper ends with the prediction of predictive maintenance become an authority mass movement in the future for the operator of the aircraft and provide them with the movement of prediction of such maintenance, in line with their budget rate.