Deep Learning Approach for Predictive Maintenance of an Aircraft Engine

Predictive maintenance has revolutionized the management of aircraft engines by introducing a proactive approach to addressing issues before they escalate. This study delves into the application of deep learning methodologies, including Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN), for the prediction of the remaining useful life (RUL) of aircraft engines. Introducing two innovative models, Convolutional LSTM (CLSTM) and Recurrent LSTM (RLSTM), we conduct a comprehensive comparative analysis with established methods to evaluate predictive accuracy. The outcomes underscore RLSTM as the top performer, achieving noteworthy accuracy, precision, and recall rates, while LSTM and CNN models also exhibit competitive performance. The effectiveness of LSTM-based architectures in capturing intricate temporal dependencies emphasize the potential of predictive maintenance to bolster aircraft reliability and safety. This study contributes significantly to advancing predictive maintenance practices within the aerospace sector, offering insights into enhancing operational efficiency and reducing maintenance expenditures.