Structural damage recognition based on deep subdomain adaptation CNN

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Journal of Vibration and Shock ›› 2025, Vol. 44 ›› Issue (3) : 251-260.

FAULT DIAGNOSIS ANALYSIS

Structural damage recognition based on deep subdomain adaptation CNN

ZHANG Jianfei*, CAO Yu

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Abstract

Aiming at the problem of poor generalization ability of the convolutional neural network (CNN) damage identification model, a structural damage identification method based on deep subdomain adaptation CNN (DSACNN) is proposed. The actual structure is taken as the target domain, the finite element model is taken as the source domain, and the source and target domains are divided into subdomains according to the damage classes.The DSACNN model is trained in an unsupervised manner to align the features of the subdomains and establish a mapping between the features and damages by minimizing the damage classification error on the source domain and the local maximum mean discrepancy between the domains. Thereby, the damage identification capability on the source domain is migrated over the target domain. The experimental results show that compared with the global domain adaptation model, the maximum improvement in accuracy of the DSACNN model based on pre-trained global domain adaptation reaches 21.8% on the simulated target domain and 9.6% on the measured target domain. It indicates that the model has stronger generalization ability.

Key words

structural damage identification / subdomain adaptation / local maximum mean discrepancy / convolutional neural networks

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ZHANG Jianfei, CAO Yu. Structural damage recognition based on deep subdomain adaptation CNN[J]. Journal of Vibration and Shock, 2025, 44(3): 251-260

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