道路传感器监测数据异常辨识方法

足尺加速加载试验场具有复杂的路面结构,其中埋设了多种传感器用于监测路面性能的各项指标.由于传感器采集的数据具有高频海量的特点,使用传统方法进行异常数据的辨识效率低且精度差.针对该问题,本文通过特定软件将原始高频采集数据进行可视化,再将得到的可视化后数据进行类别标注,以此作为原始数据集;接下来针对可视化后的数据形状特征突...

Anomaly Identification Method for Pavement Sensor Monitoring Data

The full-scale accelerated loading test field has a complex pavement structure, in which a variety of sensors are embedded to monitor indicators of pavement performance. For the high-frequency and massive data collected by the sensors, the identification of abnormal data using traditional methods has low efficiency and poor accuracy. Considering this, this study visualizes the originally collected high-frequency data through specific software and then labels the visualized data as the original dataset. Next, according to the characteristics of obvious shape features of the data after visualization, the lightweight convolutional neural network model GhostNet is selected to automatically identify the abnormal data from the monitored dataset by sensors. Through the parameter design and the network model training, the test results on the verification set show that the identification rate of abnormal data is as high as 99%. Compared with the conventional classification model residual neural network (Resnet50), the GhostNet model has improved the anomaly identification accuracy by 11%. It can quickly identify abnormal data in massive monitored data by pavement sensors, which can provide strong data support for pavement sensor fault monitoring.