基于内积矩阵及卷积自编码器的螺栓松动状态监测

BOLT LOOSENING STATE MONITORING BASED ON INNER PRODUCT MATRIX AND CONVOLUTIONAL AUTOENCODER

螺栓连接结构中的螺栓松动容易导致结构失效,如何对结构中的螺栓松动状态进行监测是当前研究的一个热点。该文利用环境激励下结构振动响应的相关性分析,结合深度学习技术,研究了一种联合使用内积矩阵(inner product matrix,IPM)和卷积自编码器(convolutional autoencoder,CAE)的神经网络模型,即基于内积矩阵及卷积自编码器(inner product matrix and convolutional autoencoder,IPM-CAE)的深度学习模型。通过对螺栓连接搭接板的螺栓松动状态监测的试验研究,验证了该方法的可行性及有效性,并与使用IPM的卷积神经网络(convolutional neural network,CNN)、堆栈自动编码器(stack autoencoder,SAE)及胶囊网络(capsule network,CapsNet)相比,IPM-CAE方法具有较快的网络训练收敛速度和较高的识别精度。

Bolt loosening of bolted connection structures can easily lead to structural failure.How to monitor the loosening state of bolts of a structure is a hot spot of current research.This paper uses the correlation analysis of structural vibration responses under environmental excitation and the deep learning technology,and studies a neural network model that uses both inner product matrix(IPM)and deep convolutional autoencoder(CAE),named inner product matrix and convolutional autoencoder(IPM-CAE)based deep learning model.This paper verifies the feasibility and effectiveness of the method through an experimental study on the bolt loosening state monitoring of a bolt connected plate.Compared with the convolutional neural network(CNN),stack autoencoder(SAE)and capsule network(CapsNet)using IPM,the proposed IPM-CAE method shows better network training convergence speed and recognition accuracy.