Abstract: Aiming at the problem of some abnormal monitoring data in the bridge health monitoring system, this paper combines time-frequency analysis and deep learning for anomaly diagnosis of monitoring data. Firstly, the data are divided and labelled according to the anomaly type, and the time-frequency analysis method is used to convert the one-dimensional bridge acceleration data into time-frequency images, and an image database is prepared for building and training deep neural network models. Then, the deep learning framework is used to build a data anomaly diagnosis model based on convolutional neural network (CNN), the training set and validation set composed of labelled data images are randomly selected and input into the model, the model weight parameters are updated and optimized through the backpropagation mechanism and Adam optimization algorithm, and batch normalization and data enhancement methods are used to improve the accuracy and generalization ability of the model. Finally, the model diagnosis results are statistically analyzed and the anomaly distribution map of the monitoring data is drawn. The results show that the proposed model has an accuracy rate of 96.69% for anomaly diagnosis of monitoring data, which realizes the rapid localization of abnormal monitoring data, and has better stability and recognition performance than the traditional deep learning model. The results of this paper can be used in the design of bridge health monitoring systems.
Keywords: data anomaly; time-frequency analysis; deep learning; bridge health monitoring
合肥工业大学学报自科版