Adversarial Fuzzy-Weighted Deep Transfer Learning for Intelligent Damage Diagnosis of Bridge With Multiple New Damages

Abstract

Recently, domain-adaptation based transfer learning has been extensively studied and successfully achieved promising results in addressing the domain drift in closed-set scenarios. However, in the bridge damage diagnosis field, the target data-sets collected from bridges frequently present samples of new damages that were not observed in the source domain, which is known as the open-set domain adaptation problem. To address this problem, this paper proposes a new open-set deep transfer learning algorithm based on joint weighted sub-domain adaptation. First, a joint weighting mechanism is proposed based on adversarial learning and fuzzy theory to represent the similarity of target-domain samples with source-domain classes, and explore the method of separating the known and unknown classes in the target domain to solve the negative transfer problem. Then, to capture the fine-grained transferable information, a sub-domain adaptation algorithm based on minimizing the multi-channel multi-kernel weighted local maximum mean discrepancy (MCMK-WLMMD) is proposed to align the corresponding sub-domains in the two domains. Finally, membership is introduced to build an unsupervised fuzzy clustering model with evaluation indicator to recognize multiple unknown damages. Extensive experiments on open-set transfer tasks between three bridges verify the effectiveness of the algorithm.