Intelligent and Precise Fault Diagnosis Strategy for Industrial Drive End Based on CNN-LSTM Architecture

In modern industry, the degree of automation and complexity of equipment is constantly increasing. Fault diagnosis is crucial for ensuring the safe and stable operation of equipment. Traditional fault diagnosis methods have limitations, while deep learning technology brings new opportunities to them. This paper proposes a fault diagnosis model based on Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM). It utilizes the powerful local feature extraction ability of CNN and the ability of LSTM to capture the long-term dependency relationship of time series data, and integrates the advantages of both to improve the diagnostic performance. The experiment uses a dataset containing ten types of fault data and trains the model after processing it in a specific way. The results show that during the training process, the loss gradually decreases and the accuracy rate continuously increases. The confusion matrix indicates that the model can effectively classify faults and has a relatively high recognition accuracy rate for most fault types. This research provides a more accurate and reliable method for the fault diagnosis of industrial equipment and has significant engineering application value.