Fault Prediction and Reliability Optimization of Aerospace Systems Based on Deep Learning

Aerospace systems are highly complex and critical, with stringent reliability requirements to ensure safe operations. The maintenance and management of these systems are of paramount importance, as any fault or failure can lead to significant consequences. Traditional fault prediction methods rely on manual inspection and rule-based systems, which are inefficient, error-prone, and reactive. The primary objective is to develop an intelligent fault prediction and reliability optimization framework for aerospace systems using DL techniques. The data are gathered from various aerospace systems, including sensor data, maintenance logs, and failure reports, sourced from operational aircraft and aerospace machinery over several years. Data were preprocessed using data cleaning and normalization methods. The research proposed a novel intelligent beluga whale optimized Elman neural network (IBWO-ENN) to identify and predict faults in aerospace systems. IBWO is used for feature selection, and ENN is employed to predict faults in aerospace systems. The aerospace propulsion system demonstrates the efficacy of the suggested model, achieving significant improvements in fault prediction accuracy (98.39%), precision (98.54%), and F1-score (98.23%) and system reliability compared to traditional methods. The research contributes to the urgent demand for improved safety and operational efficiency in increasingly complex aircraft settings by presenting a unique DL-based framework for fault prediction and reliability improvement of aerospace systems.