From Coarse to Fine: Hierarchical Zero-Shot Fault Diagnosis With Multigrained Attributes

Zero-shot fault diagnosis can identify unseen faults by predicting attributes. However, existing methods ignore the multigrained characteristics of attributes, namely, the varying levels of detail in describing fault categories. We recognize the following considerations for the first time: attributes show typical multigrained characteristics, which could be expressed in a coarse-to-fine-grained hierarchical structure; multigrained attributes play different roles in fault diagnosis, where coarse-grained attributes indicate the rough range of faults, while fine-grained attributes facilitate the precise identification of fault types. In this article, a fuzzy hierarchical zero-shot learning method is proposed to solve these issues. First, the attributes are divided into different layers according to the coarse-to-fine granularity via expert knowledge rather than being treated equally. Then, a knowledge transfer strategy is designed to transfer the knowledge from coarse-grained attributes to fine-grained ones, which can improve attribute prediction accuracy. Finally, a fuzzy inference strategy is developed to distinguish the effect of attributes with different granularity on fault inference. This strategy can identify the faults stepwise in a coarse-to-fine-grained order. The effectiveness of the proposed method is verified by a real thermal power plant process.