Abstract: As a core device in the mine power system,the operation status of the mine transformer directly affects the safety and efficiency of mine production. However,due to the complexity of the mine environment and the special nature of long-term operation of the equipment,the transformer fault diagnosis faces challenges such as high noise,data imbalance,and diverse fault types. To address these issues,a hybrid model based on Convolutional Neural Network (CNN) and Long ShortTerm Memory (LSTM),namely CNN-LSTM is proposed for the fault diagnosis of mine transformers. Firstly,CNN is utilized to extract features from the transformer operation data,effectively capturing the spatial features in the data. Subsequently,LSTM is employed to model the time series of the extracted features,identifying the dynamic change patterns in the data. The experimental results show that the proposed CNN-LSTM model achieves an average accuracy rate of over 92. 82% for diagnosing multiple fault types,significantly outperforming traditional diagnostic methods and single neural network models. This indicates that the model has significant advantages in improving diagnostic accuracy and robustness,and holds certain application prospects. 

Key words: mining transformer,fault diagnosis,convolutional neural network,long short-term memory network