基于卷积神经网络的大坝安全监测数据异常识别

为了减轻大坝安全监测数据异常识别的数据处理压力,解决传统方法难以辨别非最值异常点的问题,提出利用卷积神经网络(CNN)识别大坝安全监测数据异常模式.监测数据过程线的周期性及异常值的显著差别使CNN得以发挥图像分类功能,分别将存在单个突跳点、无异常、存在震荡段、台阶、多个突跳点、台坎的监测数据过程线作为6类图像,人工生成...

Anomaly Identification of Dam Safety Monitoring Data Based on Convolutional Neural Network

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.