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

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.