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

Abstract

Zero-shot fault diagnosis can identify unseen faults by predicting attributes. However, existing methods ignore the multi-grained characteristics of attributes, namely the varying levels of detail in describing fault categories. We recognize the following considerations for the first time: (1) attributes show typical multi-grained characteristics, which could be expressed in a coarse-to-fine-grained hierarchical structure; (2) multi-grained 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 paper, 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.