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

ZHANG Min-zhao; WANG Le; TIAN Xin-hai

doi:10.6052/j.issn.1000-4750.2021.07.0583

Volume 39 Issue 12

Nov. 2022

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Engineering Mechanics > 2022 > 39(12): 222-231. > DOI: 10.6052/j.issn.1000-4750.2021.07.0583

ZHANG Min-zhao, WANG Le, TIAN Xin-hai. BOLT LOOSENING STATE MONITORING BASED ON INNER PRODUCT MATRIX AND CONVOLUTIONAL AUTOENCODER[J]. Engineering Mechanics, 2022, 39(12): 222-231. DOI: 10.6052/j.issn.1000-4750.2021.07.0583

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

Department of Aeronautical Structural Engineering, School of Aeronautics, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China

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Received Date: July 28, 2021
Revised Date: December 03, 2021
Available Online: February 16, 2022

Abstract

Abstract

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.
- structural health monitoring,
- deep learning,
- convolutional autoencoder,
- inner product matrix,
- bolt loosening

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

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