Structural health monitoring data distortion detection method, system and device and storage medium

Abstract

The invention discloses a structural health monitoring data distortion detection method, system and device and a storage medium, and the method comprises the steps: obtaining preset original monitoring data, and carrying out the classification of the original monitoring data according to the distortion type of the original monitoring data, and obtaining first monitoring data; performing time-frequency analysis on the first monitoring data to obtain a first time-frequency graph of the first monitoring data; determining a training image set and a verification image set according to the first time-frequency graph, and establishing a first deep learning network for identifying a distortion type according to the training image set and the verification image set; and performing time-frequency analysis on the to-be-detected structural health monitoring data to obtain a second time-frequency graph of the structural health monitoring data, and inputting the second time-frequency graph into thefirst deep learning network for identification to obtain a distortion type of the to-be-detected data. The method can recognize the distortion type of the to-be-detected data, improves the efficiencyand accuracy of data distortion detection, and can be widely used in the technical field of civil engineering structure health monitoring.

Description

technical field [0001] The invention relates to the technical field of health monitoring of civil engineering structures, in particular to a method, system, device and storage medium for detecting distortion of structural health monitoring data. Background technique [0002] Structural health monitoring (SHM) aims to identify structural damage in time by monitoring structural response and evaluating structural performance in real time, and has become an important research direction in the field of civil engineering. Structural health monitoring systems are installed on more and more bridges. Among them, sensors are used to obtain structural response and other various monitoring information. Obtaining accurate data collected by sensors is an important task of SHM. However, the collected data may be affected by different factors, such as the environment, the quality of the sensors, etc. Sensor failures become more frequent compared to the lifetime of the structure. Abnorma...

AI Technical Summary

Problems solved by technology

However, these traditional detection methods have the following defects: on the one hand, traditional methods can only solve the "binary classification" problem (that is, identify as normal or abnormal); on the other hand, due to the huge amount of data in the SHM system, multiple signal processing is required Technology to detect abnormalities is inefficient, and the features extracted from massive SHM data vary greatly, making abnormal data easy to be over-processed or processed incorrectly, resulting in inaccurate detection results

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