Abstract: To accurately and effectively repair the abnormal data of the continuous monitoring dataset,a residual network data repairing（ResNet）model was proposed based on residual block optimized convolutional neural network in this paper. The engineering validation of the proposed ResNet model was carried out by using the health monitoring data of the foundation of the onshore wind turbine at Rushan Wind Farm. Some models with repair functions were also selected to repair the abnormal data of the monitoring dataset.According to the results of the repaired monitoring dataset,the comparative analysis was conducted on the repair performance and accuracy of these models. The results reveals that comparing with FCN and CNN model,ResNet model exhibits a rather high accuracy of abnormal data repair;the ResNet model is more suitable for repairing datasets where the ratio of missing or abnormal data is less than30%;the repaired results based on the ResNet model can fit better to the original curve of the continuous monitoring data,which shows a good agreement with the monitored trend.