基于加速度二次协方差矩阵和神经网络的结构损伤识别
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摘要
为了能对结构早期损伤进行有效识别,本文提出了一种基于加速度响应二次协方差(CoC)矩阵和神经网络的结构损伤识别方法。首先通过数值模拟,以白噪声作为激励,获取结构在不同损伤位置和损伤程度下的加速度响应,并计算相应的二次协方差矩阵;然后,把二次协方差矩阵作为BP神经网络的输入特征向量,对网络进行训练并对损伤位置和损伤程度同时进行识别。本文以桁架为例,将二次协方差矩阵和BP神经网络结合,对结构单损伤和多损伤分别进行识别,同时采用模态频率和模态振型与BP神经网络结合作为对比指标。对比发现:相比于模态指标,基于加速度响应二次协方差矩阵和BP神经网络的损伤识别方法,能够较好的识别结构的单损伤和多损伤,且具有更好的稳定性和抗噪性。
In order to identify structural early damage effectively, this paper proposed a method for structural damage identification based on the Covariance of Covariance(CoC) matrix of acceleration responses and neural network method. Firstly, under the white noise excitation, the acceleration responses of the structure with different damages is obtained and used to calculate the corresponding CoC matrix. Secondly, the covariance of covariance matrix was used as the input feature vector for back propagation(BP) neural network to train the network. Thirdly, the trained network is used to identify the structural damage state. In this paper, a truss structure is used as an example. The CoC matrix of acceleration responses was combined with the BP neural network to identify the damage. In the meanwhile, modal frequency and modal shape combined with BP neural network were used as the comparison index. Compared with the modal index, the damage identification method based on CoC matrix and BP network can better identify the damages, and has better stability and noise resistance ability.
In order to identify structural early damage effectively, this paper proposed a method for structural damage identification based on the Covariance of Covariance(CoC) matrix of acceleration responses and neural network method. Firstly, under the white noise excitation, the acceleration responses of the structure with different damages is obtained and used to calculate the corresponding CoC matrix. Secondly, the covariance of covariance matrix was used as the input feature vector for back propagation(BP) neural network to train the network. Thirdly, the trained network is used to identify the structural damage state. In this paper, a truss structure is used as an example. The CoC matrix of acceleration responses was combined with the BP neural network to identify the damage. In the meanwhile, modal frequency and modal shape combined with BP neural network were used as the comparison index. Compared with the modal index, the damage identification method based on CoC matrix and BP network can better identify the damages, and has better stability and noise resistance ability.
引文
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[9] LI Xueyan,LAW S S,WANG Lixin.Health monitoring of in-service bridge deck by covariance of covariance matrix of acceleration[J].Applied Mechanics and Materials,2011,7(27):4808-4814.
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[12] HUI Y,LAW S S,KU C J.Structural damage detection based on covariance of covariance matrix with general white noise excitation[J].Journal of Sound and Vibration,2017,389:168-182.
[13] 王立新,李雪艳,姜慧,等.基于加速度二次协方差矩阵参数变化比法的环境振动下结构损伤识别[J].振动与冲击,2016,35(8):143-147+158.WANG Lixin,LI Xueyan,JIANG Hui,et al.Damage identification under ambient vibration based on change ratios of covariance of covariance matrix components of structural acceleration reponses[J].Journal of Vibration and Shock,2016,35(8):143-147+158.(in Chinese)
[2] BARAI S V,PANDEY P C.Vibration signature analysis using artificial neural networks[J].Journal of Computing in Civil Engineering,1995,9(4):259-265.
[3] ELKORDY M F,CHANG K C,LEE G C.Neural networks trained by analytically simulated damage states[J].Journal of Computing in Civil Engineering,1993,7(2):130-145.
[4] 段晨东,刘义艳.基于多测点小波包特征矢量的神经网络损伤诊断方法[C]//第11届全国设备故障诊断学术会议.南京:中国振动工程学会,2008:31-33.DUAN Chendong,LIU Yiyan.A structure damage diagnosis neural network method based on feature vectors composed of wavelet packets from multi-sensors[C]//The 11th National Conference on Equipment Fault Diagnosis.Nanjing:China Vibration Engineering Society,2008:31-33.(in Chinese)
[5] 刘义艳,巨永锋,段晨东,等.基于小波包变换和神经网络的损伤诊断方法[C]//2010年第三届计算智能与工业应用国际会议,2010,168-171.LIU Yiyan,JU Yongfeng,DUAN Chendong,et al.A structure damage diagnosis method based on wavelet packet transform and neural network[C]∥Proceedings of 2010 the 3rd International Conference on Computational Intelligence and Industrial Application,2010,168-171.(in Chinese)
[6] XIE Donghai,TANG Hongwei.Application of wavelet packet transform and neural network to detect damage of elastic thin plate[J].Advanced Materials Research,2012,11(594):1105-1108.
[7] 徐菁,曲丽敏,卢翠萍.大跨度空间网格结构的健康监测系统[J].兰州理工大学学报,2016,42(4):128-133.XU Jing,QU Limin,LU Cuiping.Healthy monitor system with large-span space-netted structure[J].Journal of Lanzhou University of Technology,2016,42(4):128-133.(in Chinese)
[8] LI Xueyan,LAW S S.Matrix of the covariance of covariance of acceleration responses for damage detection from ambient vibration measurements[J].Mechanical Systems and Signal Processing,2010,24(4):945-956.
[9] LI Xueyan,LAW S S,WANG Lixin.Health monitoring of in-service bridge deck by covariance of covariance matrix of acceleration[J].Applied Mechanics and Materials,2011,7(27):4808-4814.
[10] LI Xueyan,WANG Lixin,LAW S S.Damage detection for structures under ambient vibration via covariance of covariance matrix and consistent regularization[J].Advances in Structural Engineering,2013,16(1):77-86.
[11] WANG Lixin,LI Xueyan,TAN Y,et al.Long-term health monitoring of in-service bridge deck by covariance of covariance matrix of acceleration responses[J].Advances in Structural Engineering,2015,18(12):2129-2149.
[12] HUI Y,LAW S S,KU C J.Structural damage detection based on covariance of covariance matrix with general white noise excitation[J].Journal of Sound and Vibration,2017,389:168-182.
[13] 王立新,李雪艳,姜慧,等.基于加速度二次协方差矩阵参数变化比法的环境振动下结构损伤识别[J].振动与冲击,2016,35(8):143-147+158.WANG Lixin,LI Xueyan,JIANG Hui,et al.Damage identification under ambient vibration based on change ratios of covariance of covariance matrix components of structural acceleration reponses[J].Journal of Vibration and Shock,2016,35(8):143-147+158.(in Chinese)
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