Research and Application of Intelligent Fault Diagnosis Algorithm for Electromechanical System

This article studies the research and application of intelligent fault diagnosis algorithms for electromechanical systems, and proposes an intelligent fault diagnosis algorithm that combines deep learning and transfer learning. The algorithm combines the advantages of convolutional neural networks (CNN) and long short-term memory networks (LSTM) to enhance the adaptability and diagnostic accuracy of models to different electromechanical systems. One-dimensional signal is transformed into two-dimensional time-frequency diagram by wavelet transform, fault features are extracted by CNN, and the variation law of fault with time is captured by LSTM network. In addition, transfer learning strategy is introduced to improve the generalization ability of the algorithm. In the application example of CNC machine tools, the fault diagnosis accuracy of the algorithm on the test set reaches 95%, which is significantly higher than the traditional method. The research results show that the algorithm has obvious advantages in improving the accuracy and speed of fault diagnosis, and at the same time, it enhances the reliability and stability of the electromechanical system of CNC machine tools, which provides technical support for intelligent manufacturing.