Feature Generating Network With Attribute-Consistency for Zero-Shot Fault Diagnosis

The absence of fault data in certain categories presents a significant challenge in data-driven fault diagnosis, as obtaining a complete fault dataset is often unfeasible. Zero-shot learning has emerged as a viable solution to this problem. Nonetheless, it often encounters problem of unreliable diagnosis results due to domain shift. In this article, a feature generating network with attribute-consistency is developed for zero-shot fault diagnosis, which introduces the attribute consistency constraint and feature transformation with attribute information. The implementation process comprises two parts, unseen fault class generation and discriminative feature transformation. The attribute consistency constraint adopted in data generation can make the generated data represent their attribute well. For feature transformation, a concatenation operation is used to transforming the generated samples into more discriminative representations. The effectiveness of the proposed method is verified using a public dataset for fault diagnosis purpose. Results indicate that the proposed method outperforms the state-of-art zero-shot diagnosis method.