Structure local defect detection method based on vector autoregression model

Abstract

A structure local defect recognition method based on a vector autoregression model comprises the steps: 1, collecting acceleration responses of a defect structure and a defect-free structure, and decomposing the collected acceleration responses into a plurality of samples with the same data length; 2, determining the VAR model order of each sample obtained in the step 1 by using an AIC criterion;3, according to the VAR model order determined in the step 2, establishing each sample VAR model; 4, extracting diagonal elements of the coefficient matrix in each VAR model in the step 3, and defining a brand-new feature vector f; 5, respectively calculating Mahalanobis distances of a defect state and a defect-free state according to the vector f in the step 4; and 6, performing subsequent structural defect identification according to the Mahalanobis distances of the defect state and the defect-free state calculated in the step 5. According to the method, the vibration response of the structure can be effectively utilized, the vibration response of the structure is analyzed by means of the vector autoregression model, and the local defect identification of the structure is quickly and effectively completed.

Description

technical field [0001] The invention belongs to the field of structure monitoring and monitoring. [0002] technical background [0003] Structural health monitoring (SHM) refers to the use of on-site non-destructive sensing technology to achieve the purpose of detecting structural defects or degradation through the analysis of structural system characteristics including structural response. Among them, defect identification refers to identifying whether there is a defect in the structure through certain technologies and methods, determining the location and severity of the defect, and making suggestions for the maintenance and repair of the structure, so that the structure can better exert its use value within the life cycle. Whether it is environmental vibration, wind load or earthquake load, before the dynamic effect adversely affects the structure and causes catastrophic damage, whether the defect can be quickly and effectively identified is of great significance for redu...

AI Technical Summary

Problems solved by technology

[0005] Traditional non-destructive identification technology has many limitations such as heavy workload, high cost, and small flaw detection area:

[0007] (2) The part to be tested needs to meet the requirements that the testing instrument can be directly contacted, which leads to its limitation in the application of defect detection;

[0008] (3) This method requires professional equipment support, but the equipment is often expensive and difficult to be widely promoted

[0012] (3) It is difficult to accurately obtain the benchmark finite element model of complex structures

However, most of the existing data-driven methods can only judge whether there is a defect in the structure. If you want to determine the location of the structural defect, you need to do multiple measurements and repeated calculations to complete it, which undoubtedly greatly increases the amount of calculation for defect identification.

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