The performance of power equipment fault detection models is affected by various factors including fault type,fault complexity,and image quality.Here,a fault detection model based on TrellisNet and attention mechanism is proposed for power equipment.First,Long Short-Term Memory (LSTM) is integrated with Convolutional Neural Network (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 other interference factors.In addition,the feature data obtained by LSTM-CNN are used as input,and by embedding the attention mechanism into TrellisNet,an AT-TrellisNet network with high resolution is constructed to detect the fault type of different power equipment.Finally,five common power equipment faults are selected for model validation.The experiment results show that compared with some existing detection models,the proposed model has higher detection accuracy,with a maximum of over 90％,which can meet the actual needs of power equipment fault detection.