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一种基于频率响应函数和交叉特征保证准则的损伤识别逐步方法。

A Step-by-Step Damage Identification Method Based on Frequency Response Function and Cross Signature Assurance Criterion.

作者信息

Zhan Jiawang, Zhang Fei, Siahkouhi Mohammad

机构信息

School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China.

出版信息

Sensors (Basel). 2021 Feb 3;21(4):1029. doi: 10.3390/s21041029.

DOI:10.3390/s21041029
PMID:33546231
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7913376/
Abstract

This paper aims to present a method for quantitative damage identification of a simply supported beam, which integrates the frequency response function (FRF) and model updating. The objective function is established using the cross-signature assurance criterion (CSAC) indices of the FRFs between the measurement points and the natural frequency. The CSAC index in the frequency range between the first two frequencies is found to be sensitive to damage. The proposed identification procedure is tried to identify the single and multiple damages. To verify the effectiveness of the method, numerical simulation and laboratory testing were conducted on some model steel beams with simulated damage by cross-cut sections, and the identification results were compared with the real ones. The analysis results show that the proposed damage evaluation method is insensitive to the systematic test errors and is able to locate and quantify the damage within the beam structures step by step.

摘要

本文旨在提出一种用于简支梁定量损伤识别的方法，该方法集成了频率响应函数（FRF）和模型修正。使用测量点之间的FRF的交叉特征保证准则（CSAC）指标以及固有频率来建立目标函数。发现前两个频率之间频率范围内的CSAC指标对损伤敏感。所提出的识别程序试图识别单一损伤和多重损伤。为验证该方法的有效性，对一些通过横截面模拟损伤的模型钢梁进行了数值模拟和实验室测试，并将识别结果与实际结果进行了比较。分析结果表明，所提出的损伤评估方法对系统测试误差不敏感，并且能够逐步定位和量化梁结构内的损伤。

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本文引用的文献

1
Structural Damage Localization and Quantification Based on a CEEMDAN Hilbert Transform Neural Network Approach: A Model Steel Truss Bridge Case Study.基于 CEEMDAN Hilbert 变换神经网络方法的结构损伤定位与量化：模型钢桁架桥案例研究。
Sensors (Basel). 2020 Feb 26;20(5):1271. doi: 10.3390/s20051271.
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Damage Identification in Bridges by Processing Dynamic Responses to Moving Loads: Features and Evaluation.基于动荷载下动态响应的桥梁损伤识别：特点与评估。
Sensors (Basel). 2019 Jan 23;19(3):463. doi: 10.3390/s19030463.
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Probabilistic Damage Detection of a Steel Truss Bridge Model by Optimally Designed Bayesian Neural Network.
基于最优设计贝叶斯神经网络的钢桁架桥模型概率损伤检测。
Sensors (Basel). 2018 Oct 9;18(10):3371. doi: 10.3390/s18103371.