文献类型：期刊文献

中文题名：基于属性描述的多单元过程零样本故障诊断

英文题名：Zero-Shot Fault Diagnosis for Multi-Unit Process Based on Attribute Description

作者：易永率[1];赵海涛[1]

机构：[1]华东理工大学信息科学与工程学院,上海200237

年份：2023

卷号：49

期号：6

起止页码：845

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：Scopus;北大核心:【北大核心2020】；CSCD:【CSCD_E2023_2024】；

基金：国家自然科学基金(62173143,61973122)。

语种：中文

中文关键词：零样本学习;自注意力机制;故障诊断;属性描述;多单元化工过程

外文关键词：zero-shot learning;self-attention mechanism;fault diagnosis;attribute description;multi-unit chemical process

摘要：传统的基于数据驱动的故障诊断方法依赖于大量带标签的故障样本,但在化工过程中没有目标故障样本可供训练的情形十分普遍。针对该问题,引入零样本学习(Zero-shot learning,ZSL)的思想,提出了基于属性描述的多单元自注意力机制(Attribute description based multi-unit self-attention mechanism, AMSM)的故障诊断方法。首先,语义自编码器提取样本中包含故障属性的语义信息;其次,自注意力机制利用语义信息自适应地调节各个单元特征间的相关关系进行属性学习;最后,通过比较属性矩阵的相似度实现故障诊断。基于田纳西-伊斯曼(Tennessee-Eastman, TE)过程设计了零样本故障诊断实验,结果表明AMSM能在没有目标故障样本的情况下实现故障诊断。
Traditional data-driven fault diagnosis methods rely on a large number of labeled fault samples,but the situation where there are no target fault samples for training is common in chemical processes.To address this issue,the idea of zero-shot learning(ZSL)is introduced and a fault diagnosis method based on attribute description based multi-unit self-attention mechanism(AMSM)is proposed.Firstly,the semantic autoencoder is pre-trained to extract semantic information containing fault attributes from the samples.Secondly,the self-attention mechanism adaptively assigns the attention weight of each unit to learn the correlation between units and dynamically adjusts the feature matrix to make the extracted sample features continuously match with the potential representation containing semantic information for attribute learning.Finally,fault diagnosis is achieved through determining the minimum Euclidean distance between the predicted attribute vector and the fault attribute label.To demonstrate the effectiveness of AMSM model,Tennessee Eastman(TE)process is divided into four datasets and compared with other zero-shot fault diagnosis methods and supervised learning methods.The ablation experiment is used to illustrate the importance of semantic encoder and self-attention mechanism on the model.The zero-shot fault diagnosis experiment based on TE process shows that AMSM can realize fault diagnosis without target fault samples.