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Dual-Weight Consistency-Induced Partial Domain Adaptation Network for Intelligent Fault Diagnosis of Machinery by Jiachen Kuang; Guanghua Xu; Tangfei Tao; Qingqiang Wu; Chengcheng Han; Fan Wei is a Engineering article available to read on EtoBox.

What is Dual-Weight Consistency-Induced Partial Domain Adaptation Network for Intelligent Fault Diagnosis of Machinery about?

Domain adaptation (DA)-based methods have been broadly developed for cross-domain fault diagnosis of machinery, in which the target and source label spaces are identical. However, a more challenging and practical diagnostic scenario in which the source label space subsumes target one is remaining to be solved. To address this issue, an innovative two-stage dual-weight consistency-induced partial DA (DCPDA) network, consisting of two feature extractors, a classifier, a domain discriminator, a dual-weight consistency-induced reweighting (DCR) module, and a Wasserstein distance-based-DA (WDA) module, is proposed. In the pretrained stage, a well-trained model on the source domain is acquired. In the fine-tuned stage, the DCR, which conducts a dual-weight consistency-induced weighting strategy, obtains a bi-level compound weight from both class-and samplelevel weights. Embedded with the bi-level weight, the WDA is encouraged to transfer the source knowledge with mutual agreement from double-level weights across domains. Accordingly, the DCPDA is capable of alleviating negative transfer dramatically and mapping the source diagnostic knowledge into the target domain selectively. Extensive

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Author

Jiachen Kuang; Guanghua Xu; Tangfei Tao; Qingqiang Wu; Chengcheng Han; Fan Wei

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2022

Language

EN

Field

Engineering (Physical Sciences)