Abstract: Traditional methods have difficulties in identifying the non-extreme value outliers in monitoring data of dam safety. To alleviate the pressure of data processing, we propose to use convolutional neural network (CNN) to identify the anomalies. The periodicity of process lines of monitoring data and the significant difference in outliers allow CNN to classify the process lines of monitoring data as six categories of images: process lines with single abrupt jump point, with no anomaly, with multiple abrupt jump points, with oscillating segments, with steps, and with berms. A total of 65,000 training data images and 6,500 testing data images are artificially generated. The ratio of the number of six types of images is 1∶1.5∶1∶1∶1∶1. The overall accuracy of CNN in classifying the mixed six process line images is 0.973 1, and the accuracy for each category is above 0.93. Moreover, we further improve the CNN and build an anomaly identification model by adding a function of searching the position of data anomalies. The input of the model is the process line image, while the output is the image number, image category and anomaly position.

Key words: dam safety monitoring, anomaly identification of data, convolutional neural network, image classification, non-extreme value outliers