Abstract:
The aviation industry is faced with a major challenge in achieving maximum operationalization and reliability of aircraft engines, given that unscheduled failures often result in costly downtimes, service interruptions, and present safety risks. Conventional maintenance methods, which chiefly employ a reactive method, do little to foresee problems and risk depletion of resources while increasing operational hazards. This dissertation probes the possibility of a predictive maintenance framework for turbofan aircraft engines; Long Short-Term Memory (LSTM) which is a deep learning technique. The model which is suggested is to predict the advanced pattern of wear and degradation of the engines sequentially by using a large number of historical sensor data. The prediction of potential failures is conducted with high accuracy because the model is taught to recognize the early signs of engine performance decline well in advance of occurrence. The results suggest that the model enhances existing predictive maintenance efforts by virtue of faster prediction of engine conditions and greater accuracy. The research also includes an in-depth analysis of the model's performance in various operational scenarios to ensure that it is resilient and reliable in practical terms. With these findings, the predictive maintenance system can be generalized and many more critical aviation sector systems along with external systems could be adopted, improving the efficiency and safety in aircraft operations [2]. The study proposes a novel approach to mitigate maintenance costs by bypassing unplanned downtimes and facilitating a sustainable transition in aviation operations.
