Global–Local Attention-Aware Zero-Shot Learning for Industrial Fault Diagnosis

The industrial fault diagnosis is recognized as a significant technology in industrial systems. Since the breakdown events rarely happen, the scarcity of labeled fault data poses a great challenge for industrial fault diagnosis. Zero-shot learning (ZSL) has emerged as a promising approach in industrial fault diagnosis, enabling models to classify previously unseen fault data without using labeled samples. However, existing ZSL methods primarily focus on exploiting global-level fault features, but fail to capture local-level fault features that characterize the topological relationships between neighboring data points, leading to a degradation in their generalization performance. To address these issues, we propose a global-local attention-aware ZSL (GLA-ZSL) method for industrial fault diagnosis. First, we design a global prior refinement module (GPRM) using 2D-CNN to capture the global-level fault features. Second, we present a local feature enhancement module (LFEM) based on the channel attention and 1D-CNN, which utilizes the extracted global-level fault features as input to deeply extract local-level fault features. Furthermore, a meta-learning strategy based on the prototype distance metric for ZSL is proposed to learn more discriminative global-local fault features and improve the generalization performance of the model. Finally, the proposed GLA-ZSL is evaluated on the Tennessee Eastman process (TEP) dataset, the Case Western Reserve University (CWRU) dataset, and the Jiangnan University (JNU) bearing dataset. The experimental results demonstrate that the proposed GLA-ZSL method significantly outperforms other representative ZSL methods.