Structure local defect detection method based on vector autoregression model

An autoregressive model and local defect technology, applied in measuring devices, measuring acceleration, speed/acceleration/impact measurement, etc., can solve the problems of small flaw detection area, difficult promotion, heavy workload, etc., and achieve simple calculation method and improved calculation Efficiency and applicability improvement effect

Pending Publication Date: 2019-12-10
TONGJI UNIV
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  • Description
  • Claims
  • Application Information

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 b...

Method used

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  • Structure local defect detection method based on vector autoregression model
  • Structure local defect detection method based on vector autoregression model
  • Structure local defect detection method based on vector autoregression model

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Embodiment 1

[0058] Such as figure 1 As shown, the present invention relates to a method for identifying structural local defects based on a vector autoregressive model, which is realized by the following steps, including

[0059] Step 1: collecting the acceleration α response of the defective structure and the non-defective structure, and decomposing the collected acceleration α response into multiple samples with the same data length for the establishment of the subsequent vector autoregressive model (VAR);

[0060] Step 2: Use the AIC criterion to determine the order of the VAR model of each sample obtained in Step 1,

[0061] Step 3: According to the order of the VAR model determined in Step 2, establish a VAR model for each sample,

[0062] Step 4: Extract the diagonal elements of the coefficient matrix in each of the three VAR models in step 3, and define a new eigenvector f,

[0063] Step 5: According to the vector f in step 4, respectively calculate the Mahalanobis distance of th...

Embodiment 2

[0089] In step 2 of Embodiment 1, the order of the VAR model can be determined according to the AIC criterion can be replaced by the BIC criterion. BIC (Bayesian Information Criterion) Bayesian Information Criterion is similar to AIC and is used for model selection. It was proposed by Schwarz in 1978. When training the model, increasing the number of parameters, that is, increasing the complexity of the model, will increase the likelihood function, but it will also lead to overfitting. To solve this problem, both AIC and BIC introduce a penalty item related to the number of model parameters. , the penalty term of BIC is larger than that of AIC, considering the number of samples. When the number of samples is too large, it can effectively prevent the model complexity from being too high due to high model accuracy.

[0090] BIC=kln(n)-2ln(L)

[0091] Among them, k is the number of model parameters, n is the number of samples, and L is the likelihood function. The kln(n) penalt...

Embodiment 3

[0093] The CUSUM control chart method in Step 6 of Embodiment 1 can be replaced by EWMA.

[0094] EWMA (Weighted Moving Average) is a type of time-weighted control chart in which an exponentially weighted moving average is plotted. Each EWMA point combines information from all previous subgroups or observations according to a user-defined weighting factor. The advantage of EWMA charts is that they are not heavily affected when small or large values ​​enter the calculation. By varying the weights used and the number of s to control the limit, a control chart can be constructed that can detect shifts in the process of almost any size. For this reason, EWMA control charts are often used to monitor controlled processes to detect small excursions from the target. Its EWMA statistic Z is calculated as follows:

[0095] Z 0 =λMD 1

[0096] Z j =λMD j+1 +(1-λ)MD j-1 , 0.05≤λ≤0.25

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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...

Claims

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Application Information

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IPC IPC(8): G06F17/50G01P15/00
CPCG01P15/00
Inventor 唐和生赵涛涛
Owner TONGJI UNIV
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