In the task of power equipment fault detection, the performance of the model is affected by various factors, such as the diversity of fault types, the complexity of fault characteristics, and the differences in image quality. To solve these problems, a new power equipment fault detection model based on TrellisNet and attention mechanism is proposed. The model first integrates Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) to construct LSTM-CNN to obtain fault characteristics in images, which can effectively distinguish features of different fault types and reduce the influence of noise and interference factors. In addition, the feature data obtained by LSTM-CNN is used as input, and by embedding the attention mechanism into TrellisNet, an AT-TrellisNet network with high resolution is constructed to detect the fault types of different power equipment. Finally, by selecting five common power equipment faults for model verification, the experimental results show that the model has a higher detection accuracy rate of over 90% compared to some existing detection models, which can meet the actual power equipment fault detection requirements.