Data Anomaly Detection of Bridge Structures Using Convolutional Neural Network Based on Structural Vibration Signals

Yixiao Zhang,Ying Lei

doi:10.3390/sym13071186

Abstract

Structural monitoring provides valuable information on the state of structural health, which is helpful for structural damage detection and structural state assessment. However, when the sensors are exposed to harsh environmental conditions, various anomalies caused by sensor failure or damage lead to abnormalities of the monitoring data. It is inefficient to remove abnormal data by manual elimination because of the massive number of data obtained by monitoring systems. In this paper, a data anomaly detection method based on structural vibration signals and a convolutional neural network (CNN) is proposed, which can automatically identify and eliminate abnormal data. First, the anomaly detection problem is modeled as a time series classification problem. Data preprocessing and data augmentation, including data expansion and down-sampling to construct new samples, are employed to process the original time series. For a small number of samples in the data set, randomly increase outliers, symmetrical flipping, and noise addition methods are used for data expansion, and samples with the same label are added without increasing the original samples. The down-sampling method of symmetrically extracting the maximum value and the minimum value at the same time can effectively reduce the dimensionality of the input sample, while retaining the characteristics of the data to the greatest extent. Using hyperparameter tuning of the classification weights, CNN is more effective in dealing with unbalanced training sets. Finally, the effectiveness of the proposed method is proved by the anomaly detection of acceleration data on a long-span bridge. For the anomaly detection problem modeled as a time series classification problem, the proposed method can effectively identify various abnormal patterns.

Highlights

In the field of structural health monitoring, the problem of data accumulation has been paid more and more attention
Cui et al [17] proposed a sliding window method combined with a Multi-scale Convolutional Neural Network (MCNN) to solve the time series classification problem and achieved good results on a large number of benchmark data sets
The structure starts with a stack of convolutional and pooling layers, and connected to a flatten layer to convert twodimensional features into one-dimensional output, and multiple dense layers can be aFFdiigdgueurdreef55o..rWWclooarrskksfflilfooiwwcaotoiffottnhheeoprprrrooeppgoorseseesddsimomneet.htHhoodod.w. ever, there is a little difference between them: one-dimensional convolutional neural networks can use larger convolution kernels [21]

Summary

Introduction

In the field of structural health monitoring, the problem of data accumulation has been paid more and more attention. Tang et al [12] proposed a new anomaly detection method using computer vision and deep learning methods This method first converts the original time series data into images, imitating human-vision-based data collection, and trains CNN for abnormal classification. Cui et al [17] proposed a sliding window method combined with a Multi-scale Convolutional Neural Network (MCNN) to solve the time series classification problem and achieved good results on a large number of benchmark data sets. Wen et al [19] used data augmentation methods such as random mutation and adding random trends in different data sets and proposed a time series segmentation approach based on convolutional neural networks (CNN) and transfer learning. The main span of the bridge is 1088 m long, and the two side spans are 300 m

Data Anomaly Classification Method Based on 1D-CNN

Data Augmentation

Square

Flatten

Findings

Conclusions