FPRAN: A Hierarchical Generative Model for Few-Shot Pantograph Fault Diagnosis

Pantograph is the crucial component of rail transit vehicles. Detecting and resolving faults swiftly is important in the pantograph. Existing fault diagnosis technologies with artificial intelligence have difficulties in dealing with some rare faults due to the scarcity of training samples. This paper proposes a new algorithm for few-shot fault diagnosis of pantograph. The proposed method constructs a reconstructive adversarial network with hierarchical structure to mine multi-scale feature from few-shot samples. A hierarchical data generation module is designed leveraging adversarial learning to generate scarce category samples. An initial classifier is employed to constrain the generated results, while the classifier is dynamically reconstructed to enhance overall performance. This paper designs detailed experiments to demonstrate that, compared with other existing methods, the proposed algorithm can better solve the problem of pantograph fault diagnosis.