Fault prediction of complete electrical engineering equipment based on deep learning technology

Abstract In order to ensure the safe operation of equipment, extend its lifespan, and reduce maintenance costs, the author conducted research on fault prediction of electrical engineering complete equipment based on deep learning technology. Firstly, by analyzing the working principle and fault characteristics of the equipment, determine the data features suitable for fault prediction; Then, deep learning models such as Convolutional Neural Networks (CNN) and Long Short Term Memory Networks (LSTM) are used to analyze the operational data of the equipment and achieve fault prediction; Finally, through comparative experiments, the results showed that the CNN model achieved good performance in fault prediction tasks, with a recall rate of 0.86, an accuracy rate of 0.85, and an F1 value of 0.85. This indicates that the CNN model can effectively capture local features in device signals: (2) The LSTM model performs slightly better than the CNN model in fault prediction tasks, with a recall rate of 0.89, an accuracy rate of 0.88, and an F1 value of 0.88. This indicates that the LSTM model has advantages in capturing temporal features: (3) The CNN LSTM fusion model combines the advantages of CNN and LSTM models, with a recall rate of 0.93, accuracy rate of 0.92, and Fl value of 0.92, which is significantly better than the performance of using CNN or LSTM alone. This indicates that feature fusion can further improve the accuracy of fault prediction.