A Stacked Generalized Zero-Shot Learning Framework for Fault Diagnosis of High-Speed Train Bogies With Data Imbalance

In fault diagnosis for high-speed trains (HSTs), data-driven methods are becoming increasingly popular. However, diagnosing unknown faults remains challenging due to limitations of existing generalized zero-shot learning (GZSL) methods, such as prediction confidence shifts from data imbalance, and domain shift between known and unknown classes, making it an open problem in engineering applications. This article addresses this issue by proposing a stacked GZSL (stacked-GZSL) framework, which combines attribute-based GZSL strategies and generative model-based GZSL strategies. The former leverages the correlations among data attributes and categories to tackle feature distribution differences across classes, while the latter addresses weight drift issue caused by the imbalance within known data and between known and unknown classes. Then, stacked model techniques are employed to resolve compatibility issues and further enhance accuracy. Experimental results confirm that the proposed framework can effectively address the challenge of unknown compound faults in train bogies under data imbalance. The framework achieves over 90% accuracy for known faults and 70% for unknown faults, outperforming single GZSL strategies and other classic ensemble models.