文献类型：期刊文献

英文题名：Semantic-Consistent Embedding for Zero-Shot Fault Diagnosis

作者：Hu, Zhengwei[1];Zhao, Haitao[1];Yao, Lujian[1];Peng, Jingchao[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2023

卷号：19

期号：5

起止页码：7022

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

收录：;EI(收录号：20224112878135);WOS:【SCI-EXPANDED(收录号:WOS:000982913400070)】；

基金：This work was supported by National Natural Science Foundation of China (NSFC) under Grants 62173143 and 61973122.

语种：英文

外文关键词：Semantics; Fault diagnosis; Training; Informatics; Task analysis; Loss measurement; Knowledge based systems; Barlow matrix; fault diagnosis; semantic-consistent embedding (SCE); zero-shot learning

摘要：In the traditional fault diagnosis task, it is difficult to collect training samples to exhaust all fault classes. There are massive target faults that cannot be collected in advance, which may restrict the performance of fault diagnosis methods. In this article, a novel method named semantic-consistent embedding (SCE) is proposed for zero-shot industrial fault diagnosis. SCE tries to classify unseen class faults only by using seen class faults for training. The fault samples and their human-specified attribute vectors are embedded into a semantic-consistent space and then reconstructed from that space. A specific Barlow matrix is designed to measure the consistency between the embedding of fault samples and the embedding of attribute vectors. The diagonal elements and the off-diagonal elements of the Barlow matrix encode the within-dimension consistency and between-dimension consistency of the cross-modal embeddings, respectively. Through optimizing the Barlow matrix to an identity matrix, SCE learns a significant space where the cross-modal embeddings have consistent representation while reducing the redundant components. Extensive experiments show that SCE gets significant superiority on the three-phase transmission system (26.9% gains) and the Tennessee Eastman process (15.5% gains). Moreover, SCE even gets competitive results with supervised learning methods.