A Novel Zero-Shot Learning Method With Feature Generation for Intelligent Fault Diagnosis

In the traditional data-driven fault diagnosis task, gathering training samples for all possible fault classes poses a significant challenge. There are many target faults that cannot be collected in advance, which potentially limiting the performance of fault diagnosis models. Zero-shot learning has emerged as a viable solution to this problem. However, it often encounters the issue of domain shift. In this article, an attribute-consistent generative adversarial network with feature generation (ACGAN-FG) is proposed for zero-shot fault diagnosis. ACGAN-FG introduces a discriminative classifier and a binary comparator to construct the attribute-consistent losses, which can alleviate the issue that the generated features may deviate from real faults. To generate fault features with greater diversity and enhance the robustness of the proposed model, a cycle rank loss is designed. Besides, this method also introduces feature concatenation to build new training data and testing data. This concatenation can transform the generated features into more discriminative representation for further fault diagnosis. The effectiveness of the proposed method is validated on two cases for fault diagnosis purpose. The results also indicate that the proposed method is outperforms other state-of-art zero-shot fault diagnosis methods.