Mechanical Parts Life Prediction and Health Monitoring System Based on Deep Learning

In order to improve the ability of accurate prediction and real-time monitoring of the working state of mechanical parts, firstly, a deep learning model was constructed to extract the features of mechanical parts by using convolutional neural network structure, and then a deep learning model was designed to input the extracted features into the fully connected layer for information fusion and classification, so as to realize the life prediction and health status monitoring of mechanical parts. Then, the trained deep learning model is integrated with the monitoring system to form a complete mechanical parts life prediction and health monitoring system. Finally, the system is continuously optimized and updated to improve the prediction accuracy and real-time performance and adapt to different working conditions and environmental changes. The results showed that the MAE and RMSE values of the deep learning model were 2.1 and 2.5, respectively, and the MAPE was 10%. It shows excellent performance and provides important technical support and application prospect for engineering field.