This study constructs a prediction model integrating Long Short-Term Memory (LSTM) Network and Convolutional Neural Network (CNN)

designs mechanisms for multi-source data feature extraction and model training

outputs indicators of fault probability and remaining useful life (RUL)

and embeds the model into an online monitoring platform to realize real-time early warning and automatic generation of maintenance strategies

effectively improving the operational reliability of equipment and the level of intelligent operation.