Category-tree-guided hierarchical knowledge transfer framework for zero-shot fault diagnosis

Abstract

Zero-shot learning (ZSL) can diagnose unseen faults without corresponding training data, which has aroused the researchers’ interest. However, a prevailing challenge in most existing ZSL approaches is their limited effectiveness in distinguishing similar unseen faults. This paper proposed a category-tree-guided hierarchical knowledge transfer zero-shot fault diagnosis (CTZSD) method, which is a coarse-to-fine zero-shot fault diagnosis framework to alleviate this problem. To embody the similarities between fault categories, the concept of fault category tree is proposed, for which a data-attribute collaborative tree construction mechanism (DATC) is designed. Rather than relying solely on semantic knowledge, DATC involves data, which carries richer information, to complement the category similarity evaluation. A hierarchical knowledge transfer zero-shot fault diagnosis mechanism (HKT) is subsequently developed, utilizing the established category tree to gradually narrow down the options, thereby promoting the recognition of similar unseen faults. The mechanism treats the diagnostic outcomes and model parameters from coarse-grained tasks as knowledge and transfers them to fine-grained tasks for guidance, realizing a coarse-to-fine diagnosis. Aiming at providing discriminative information to further distinguish similar unseen faults, attention modules are integrated within HKT. These modules assess attribute weight, thereby directing the model’s focus toward the discriminative attributes of similar unseen faults. Experiments on a real TPP industrial process demonstrate that the proposed CTZSD outperforms other traditional ZSL methods in distinguishing similar unseen faults, improving the average accuracy by at least 19.7%.