  Domain generalization achieves fault diagnosis on unseen modes. In process
industrial systems, fault samples are limited, and only single-mode fault data
can be obtained. Extracting domain-invariant fault features from single-mode
data for unseen mode fault diagnosis poses challenges. Existing methods utilize
a generator module to simulate samples of unseen modes. However, multi-mode
samples contain complex spatiotemporal information, which brings significant
difficulties to accurate sample generation. Therefore, double gradient reversal
network (DGRN) is proposed. First, the model is pre-trained to acquire fault
knowledge from the single seen mode. Then, pseudo-fault feature generation
strategy is designed by Adaptive instance normalization, to simulate fault
features of unseen mode. The dual adversarial training strategy is created to
enhance the diversity of pseudo-fault features, which models unseen modes with
significant distribution differences. Subsequently, domain-invariant feature
extraction strategy is constructed by contrastive learning and adversarial
learning. This strategy extracts common features of faults and helps multi-mode
fault diagnosis. Finally, the experiments were conducted on Tennessee Eastman
process and continuous stirred-tank reactor. The experiments demonstrate that
DGRN achieves high classification accuracy on unseen modes while maintaining a
small model size.
