| Summary: | The reliable detection of faults and anomalies in aircraft engines is vital for ensuring aviation safety and operational efficiency. Traditional methods often struggle with the complexity of data from modern engines, necessitating innovative approaches. In recent years, deep learning, particularly Deep Neural Networks (DNN), has shown promise in revolutionizing fault detection. This paper aims to explore and implement DNN-based models for aircraft engine failure detection. Leveraging DNN's capabilities for handling high-dimensional data and creating non-linear decision boundaries, the proposed system seeks to enhance safety, reduce maintenance costs, and improve operational efficiency in aviation utilizing DNN-based model development. Rigorous training and validation will ensure efficiency, accuracy, and robustness. The system's integration allows for real-time monitoring and precise detection of rare and critical engine failures. The results show the contribution of the proposed method to aviation safety for fault detection, promising proactive maintenance, operational excellence, and heightened confidence in passenger safety.
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