Imbalanced open set domain generalization network for sensor fault diagnosis

In recent years, the technique of applying domain generalization methods to solve cross-domain fault diagnosis problems has received widespread attention in the industrial community, among which, the open-set domain generalization fault diagnosis method effectively copes with the occurrence of unknown fault states in the target domain. However, issues such as data imbalance where fault data are scarce and normal data are abundant during the long-term operation of industrial sensors, and boundary shifts caused by unknown faults occurring in the target domain, make it difficult for the existing open-set domain generalization techniques to achieve accurate decision-making on sample types. This paper therefore introduces the HSL-ARAN generalization network, which can be generalized to carry out unknown fault diagnosis under imbalanced data conditions. First, a hierarchical style learning network is designed to encourage the generation of samples with relatively rich feature information, to address the issue of class imbalance in the source domain. Then, adversarial training with uncertainty weighting is used to extract reliable domain-invariant representations, and the inter-class relationships are leveraged to determine appropriate class boundaries and rejection thresholds. Finally, a new local clustering method is employed to further enhance the reliability of the class boundaries, which enables the identification of new fault modes. The algorithm is tested on sensor data for a nickel flash furnace system, and the effectiveness and superiority of the HSL-ARAN diagnosis method are verified.