Domain fuzzy generalization networks for semi-supervised intelligent fault diagnosis under unseen working conditions

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 outputting onedimensional domain probability is innovatively substituted by a domain classifier that predicts the domain probabilities belonging to all the source domains. Then, the domain fuzzy strategy is established in domain-adversarial training to extract the domain-invariant features with finegrained distribution alignment. Finally, the metric learning is embedded in feature extractor to extract the discriminative features from a class-level optimization perspective. Benefited from the extracted domain-invariant and discriminative features, the proposed DFGN model exhibits strong generalization ability that can be effectively applied to intelligent fault diagnosis under unseen working conditions. The advantages and superiority of the proposed method over state-ofthe-art semi-supervised domain generalization methods are confirmed by extensive generalization experiments on two bearing datasets.