Task-Generalization-Based Graph Convolutional Network for Fault Diagnosis of Rod-Fastened Rotor System

The rod-fastened rotor is a critical component of heavy-duty gas turbines, and therefore, it is imperative to investigate its failure mechanisms and develop intelligent diagnostic techniques. The issue of effective cross-domain diagnosis has garnered significant interest considering the changing operating conditions of rod-fastened rotors. To mitigate the effects of limited data samples or high collection costs in the target domain, we introduce a fault diagnosis model named the task-generalization-based graph convolutional network (TG-GCN), which leverages adversarial perturbations within a meta-learning framework to enhance the robustness of prior knowledge incorporated into the graph convolutional network. Initially, the method involves generating adversarial training samples in proximity to the distribution of the source-domain data by optimizing the worst case formulation to enhance generalization to task distribution. Next, the support set and the query set are simultaneously fed into the feature extraction network, yielding corresponding feature vectors. Subsequently, similarities between feature vectors and the degree of dispersion are computed in the task space, serving as connection weights for constructing the graph network model aimed at executing fault diagnosis. The proposed TG-GCN framework employs the introduction of data augmentation during the iteration process, which helps to strengthen the vulnerable components of the network by connecting them with the worst case perturbations. Moreover, we employed a rod-fastened rotor experimental system to elucidate the underlying failure mechanisms and corroborate the superiority of the TG-GCN.