王慧, 王乐, 田鑫海. 基于相关函数矩阵及卷积神经网络的结构健康监测研究[J]. 工程力学, 2023, 40(5): 217-227. DOI: 10.6052/j.issn.1000-4750.2022.01.0016
引用本文: 王慧, 王乐, 田鑫海. 基于相关函数矩阵及卷积神经网络的结构健康监测研究[J]. 工程力学, 2023, 40(5): 217-227. DOI: 10.6052/j.issn.1000-4750.2022.01.0016
WANG Hui, WANG Le, TIAN Xin-hai. STRUCTURAL HEALTH MONITORING BASED ON CORRELATION FUNCTION MATRIX AND CONVOLUTIONAL NEURAL NETWORK[J]. Engineering Mechanics, 2023, 40(5): 217-227. DOI: 10.6052/j.issn.1000-4750.2022.01.0016
Citation: WANG Hui, WANG Le, TIAN Xin-hai. STRUCTURAL HEALTH MONITORING BASED ON CORRELATION FUNCTION MATRIX AND CONVOLUTIONAL NEURAL NETWORK[J]. Engineering Mechanics, 2023, 40(5): 217-227. DOI: 10.6052/j.issn.1000-4750.2022.01.0016

基于相关函数矩阵及卷积神经网络的结构健康监测研究

STRUCTURAL HEALTH MONITORING BASED ON CORRELATION FUNCTION MATRIX AND CONVOLUTIONAL NEURAL NETWORK

  • 摘要: 环境激励下利用时域振动响应构建的内积矩阵是结构健康监测中一种较好的结构特征参数。为了提升结构健康监测方法的识别准确率,构建内积矩阵时往往需要较多的振动响应测点,这将直接影响方法的工程实用性。该文基于时域振动响应的相关性分析理论,将内积矩阵扩展到了相关函数矩阵,实现从少量的振动响应测点中获取更多的结构健康特征信息,以降低结构健康监测方法对测点数量的需求。进一步结合卷积神经网络优异的数据特征提取能力,以相关函数矩阵为输入、结构健康状态为输出,提出了基于相关函数矩阵及卷积神经网络的结构健康监测方法。典型航空加筋壁板螺栓松动监测的实验研究结果表明,仅采用结构上任意2个测点的时域振动响应,该文方法针对螺栓松动位置的识别准确率可达99%以上。

     

    Abstract: The inner product matrix constructed by time domain vibration response under ambient excitation is a good structural characteristic parameter in structural health monitoring. In order to improve the identification accuracy of the structural health monitoring method using inner product matrix, more vibration response measurement points are often needed, which will directly affect the engineering practicability of the method. Based on the correlation analysis theory of time domain vibration responses, the inner product matrix is extended to the correlation function matrix to obtain more structural health characteristic information from a small number of vibration response measurement points, and the requirement of the number of measurement points for the structural health monitoring method will be reduced. Furthermore, combining with the excellent data feature extraction capability of convolutional neural network, a structural health monitoring method based on correlation function matrix and convolutional neural network is proposed with correlation function matrix as input and structural health status as output. The experimental results of the bolt loosening monitoring of a typical aeronautical stiffened panel show that the identification accuracy of the proposed method for bolt loose position can reach more than 99% by using only the time domain vibration responses of any two measurement points.

     

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