**4. Conclusions**

This paper modeled the anomaly detection problem into a time series classification problem. The original time series undergoes data preprocessing and data augmentation to ge<sup>t</sup> a sample set with more uniform distribution, more obvious features, and smaller dimensions. Data augmentation includes data expansion and down-sampling. For small samples, the methods of symmetrical flipping, adding noise, and randomly generating outliers 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 and minimum values can effectively reduce the dimensionality of the input sample and retain its features. Build a one-dimensional convolutional neural network model that is faster for time series classification problems. Adding the hyperparameter tuning of class weights makes the network more effective in dealing with an unbalanced training set. The method is verified with the acceleration data of a long-span cable-stayed bridge for one month. For the anomaly detection problem modeled as a time series classification problem, the results show that the proposed method can automatically detect a variety of data anomaly categories with high precision.

The proposed method can accurately identify most types of abnormal data, but for abnormal types with very inconspicuous features, such as outlier data, there is still much room for improvement in recognition accuracy. In future work, time series augmentation will not only be carried out in the time domain, but will be expanded to the frequency domain, or more advanced methods (such as GAN) will be used to expand samples.

**Author Contributions:** Conceptualization, Y.Z. and Y.L.; methodology, Y.Z.; validation, Y.Z. and Y.L.; investigation, Y.Z.; data curation, Y.Z.; writing—original draft preparation, Y.Z.; writing—review and editing, Y.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Key R&D Program of China via Grant No. 2018YFC0705606.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Restrictions apply to the availability of these data. Data was obtained from The 1st International Project Competition for Structural Health Monitoring with the permission of The 1st International Project Competition for Structural Health Monitoring organizing committee.

**Acknowledgments:** The raw data involved in this study were obtained from the organizing committee of the 1st International Project Competition for Structural Health Monitoring (IPC-SHM (2020)), and the authors thank IPC-SHM (2020) organizing committee for its valuable data resources.

**Conflicts of Interest:** The authors declare no conflict of interest.
