Generative model combining cross-modal reconstruction and semantic alignment for generalized zero-shot fault diagnosis

Abstract To address the problem of simultaneously identifying both seen and unseen classes of faults in zero-shot fault diagnosis (ZSFD), this paper proposes a generative model combining cross-modal reconstruction and semantic alignment (CRSA-GM) for generalized ZSFD. First, a cross-modal alignment module based on the Barlow matrix is designed to learn fault representations with lower redundancy and higher consistency to enhance diagnostic performance. Then, a generation strategy based on the center-consistency assumption is introduced, which integrates fault information from both the feature space and the attribute space to synthesize samples of unseen fault classes. Finally, an incremental learning strategy is adopted, which is designed to actively learn from the synthetic samples to acquire knowledge about unseen fault classes. The cases on the three-phase transmission system and the Tennessee Eastman process demonstrate that the proposed CRSA-GM exhibits remarkable superiority and effectiveness in both zero-shot and generalized zero-shot fault diagnosis.