Advanced fault diagnosis and prognosis model for UAV control systems

Unmanned aerial vehicle UAV dependability and safety must be guaranteed, particularly for autonomous operations. In order to forecast the Remaining Useful Life RUL of crucial UAV components, this work presents an Advanced Fault Diagnosis and Prognosis Model that combines rule-based fault diagnosis, deep learning-based prognosis, and real-time problem detection. The advanced fault diagnosis and prognosis model presented in this research is intended to identify, categorise, and forecast defects in UAV control systems. The suggested framework combines rule-based fault diagnosis for sensor and actuator fault classification, statistical threshold-based anomaly identification for real-time fault detection, and an LSTM network for critical component Remaining Useful Life RUL prediction. An anomaly detection module based on autoencoders improves the system's capacity to discover intricate and unknown flaws. The model uses a sliding window technique to handle real-time sensor data, producing outputs like RUL predictions, fault classifications, and deviation charts. The efficacy of the model in enhancing UAV reliability through precise defect identification, diagnosis, and prognosis is demonstrated by validation using both synthetic and real-world UAV datasets. The framework continually processes sensor data in real time through a sliding window technique. Sensor deviation graphs, autoencoder reconstruction errors, and RUL forecasts are among the outputs that give operators concise, useful information. Real-world UAV sensor data and synthetic datasets are used to validate the model, which shows strong performance in fault detection, classification, and prognosis.