Affiliations: [a] Hebei Province Civil Engineering Monitoring and Evaluation Technology Innovation Center, Hebei University, Baoding, Hebei, China
| [b] School of Cyber Security and Computer Science, Hebei University, Baoding, Hebei, China
| [c] College of Civil Engineering and Architecture, Hebei University, Baoding, Hebei, China
Correspondence:
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Corresponding authors. Zhen Lei, Hebei Province Civil Engineering Monitoring and Evaluation Technology Innovation Center, Hebei University, Baoding 071002, Hebei, China. E-mail: leizhen@hbu.edu.cn. and Liang Zhu, School of Cyber Security and Computer Science, Hebei University, Baoding 071002, Hebei, China. E-mail: zhu@hbu.edu.cn.
Abstract: Pattern recognition technology is applied to bridge health monitoring to solve abnormalities in bridge health monitoring data. Testing is of great significance. For abnormal data detection, this paper proposes a single variable pattern anomaly detection method based on KNN distance and a multivariate time series anomaly detection method based on the covariance matrix and singular value decomposition. This method first performs compression and segmentation on the original data sequence based on important points to obtain multiple time subsequences, then calculates the pattern distance between each time subsequence according to the similarity measure of the time series, and finally selects the abnormal mode according to the KNN method. In this paper, the reliability of the method is verified through experiments. The experimental results in this paper show that the 5/7/9 / 11-nearest neighbors point to a specific number of nodes. Combined with the original time series diagram corresponding to the time zone view, in this paragraph in the time, the value of the temperature sensor No. 6 stays at 32.5 degrees Celsius for up to one month. The detection algorithm controls the number of MTS subsequences through sliding windows and sliding intervals. The execution time is not large, and the value of K is different. Although the calculated results are different, most of the most obvious abnormal sequences can be detected. The results of this paper provide a certain reference value for the study of abnormal detection of bridge health monitoring data.
Keywords: Artificial intelligence, bridge health monitoring, data anomaly detection, KNN algorithm, multivariate time series
