Graph Neural Networks with Generative Adversarial Networks for Semi-Supervised Fault Diagnosis

In conventional fault diagnostic methods, supervised learning-based approaches may not be applicable to practical systems because of the extensive requirements for labeled data. Moreover, conventional approaches have not adequately addressed the challenges posed by sparsely labeled and imbalanced datasets. To address these limitations, we propose a semi-supervised fault diagnostic method based on graph convolutional networks with generative adversarial networks. Distinct from conventional methods, the proposed method instructs a discriminator to extract features from labeled and unlabeled data. The discriminator is employed to construct a similarity matrix to enhance the efficacy of graph-based methods. A graph-based classifier with a discriminator can efficiently perform fault diagnosis without requiring data augmentation. The fault diagnostic methods were evaluated in terms of their classification accuracy to validate the superiority of the proposed method. The simulation results confirm that the proposed method can improve classification accuracy by up to 66% compared with conventional methods.