Fault Prediction and Health Management of Electromechanical Systems: a Machine Learning Approach

Aviation electromechanical systems, as critical subsystems of aircraft, operate in complex operational environments and are prone to diverse fault patterns. Traditional fault prediction methods have limitations in terms of accuracy and real-time performance, making it difficult to meet the maintenance requirements of modern aviation electromechanical systems. In this paper, we propose a hybrid model combining Support Vector Machine (SVM) and Long Short-Term Memory Network (LSTM) for fault prediction and health management (PHM) of aviation electromechanical systems. Experimental validation demonstrates that this model achieves superior performance in terms of training accuracy and loss values, providing theoretical support and technical guidance for the intelligent maintenance of aviation electromechanical systems.