Bridge strain data abnormal value identification method based on data relevance

Abstract

The invention discloses a bridge strain data abnormal value identification method based on data relevance. The bridge strain data abnormal value identification method comprises the steps of obtaining the sensor original data installed at the same position; extracting the long-period trend data from the original strain data through the wavelet decomposition to obtain a difference value between the original strain data and the long-period trend data; according to the mean value and the standard deviation of the strain difference value data, obtaining a change range of the potential abnormal values in the original strain data; extracting the long-period trend data from the original acceleration data through wavelet decomposition to obtain a difference value between the original acceleration data and the long-period trend data; obtaining a change range of a potential abnormal value in the original acceleration data according to a mean value and a standard deviation of the acceleration difference data; comparing the occurrence position of the potential abnormal value in the original strain data with the occurrence position of the potential abnormal value in the original acceleration data, and judging and identifying the true abnormal value of the original strain data. According to the method, the problem of the strain data abnormal values in the bridge health monitoring can be accurately and efficiently solved.

Description

technical field [0001] The invention belongs to the field of analysis and research of bridge health monitoring data, in particular to a method for identifying abnormal values ​​of bridge strain data based on data correlation. Background technique [0002] The application of bridge health monitoring technology at home and abroad has gradually matured, and the functions of large-scale bridge structure health monitoring systems have gradually improved. However, due to the increasing number of its components and influencing factors, the potential for failures has gradually increased. The contradiction between the reliability and the limited life of the monitoring system equipment is more prominent. For example, the failure of individual components will often cause a chain reaction, resulting in the failure of the entire system to operate or even paralysis. In particular, if the monitoring data distortion caused by the monitoring system's own failure cannot be detected in time, i...

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Problems solved by technology

However, the strain data itself fluctuates greatly, and when a vehicle passes by, it will cause large signal fluctuations. Such signal fluctuations and data outliers are difficult to distinguish from a single signal source.

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