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Domain Fuzzy Generalization Networks for Semi-supervised Intelligent Fault Diagnosis Under Unseen Working Conditions by He Ren; Jun Wang; Zhongkui Zhu; Juanjuan Shi; Weiguo Huang is a Engineering article available to read on EtoBox.

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In recent years, domain adaptation methods have made remarkable achievements in fault diagnosis under variable working conditions. However, the methods usually fail when target data are unavailable for model training. Confronting with the problem of intelligent fault diagnosis for unseen working conditions, domain generalization methods have been gradually explored. Most existing domain generalization fault diagnosis methods are supervised learning models that require multiple fully labeled source domains. Few studies have been done on semi-supervised domain generalization when only partial source domains have class labels, which is generally a practical industrial scenario because labeling industrial data is a laborious work and requires scarce domain experts. Consequently, this paper proposes a novel semi-supervised domain generalization framework, named domain fuzzy generalization networks (DFGN), for intelligent fault diagnosis under unseen working conditions. The main idea of the DFGN is to enhance the capabilities of learning domain-invariant and discriminative features by proposing domain fuzzy and metric learning strategies. First, the traditional domain discriminator outpu

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Author

He Ren; Jun Wang; Zhongkui Zhu; Juanjuan Shi; Weiguo Huang

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Engineering (Physical Sciences)