Intelligent anomaly detection for dynamic high-frequency sensor data of road underground structure

Lili Pei, Zhaoyun Sun, Ronglei Li, Wei Guan, Yulong Wu, Wei Li

Abstract: The structural health monitoring data of the Research Institute of Highway Ministry of Transportation Track (RIOHTRACK) are huge and complex, including a large amount of dynamic high-frequency sensor data of road underground structures. However, detecting anomalies in the overall distribution of the whole loading cycle data is difficult for traditional numerical data analysis methods. This study proposes an anomaly detection method that visualizes numerical values and designs a deep convolutional neural network DCNN6 for image classification to achieve anomaly detection of large-scale dynamic high-frequency sensor data. After training, the detection rate of DCNN6 for abnormal data reached 92.3% for the validation set. Compared with Residual Neural Network (ResNet50) and GhostNet, the detection accuracy of the method proposed in this study increased by 69% and 4%, respectively, reaching 97%, and the detection speeds were also faster by 5 s/epoch and 4 s/epoch, respectively. Therefore, the proposed method can accurately and quickly detect the abnormality of the dynamic high-frequency sensor data of underground structures, which can provide data support for quickly discovering that the vehicle deviates from the preset trajectory, rectifying the driver's driving deviation, analyzing the force of the whole road area, and grasping the evolution law of the rut.