文献类型：期刊文献

英文题名：Fault Diagnosis Strategy for Few Shot Industrial Process Based on Data Augmentation and Depth Information Extraction

作者：Tian, Ying[1]; Xiang, Xin[1]; Peng, Xin[2]; Zhong, Yin[1]; Zhang, Wei[1]

机构：[1] Department of control science and Engineering, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China; [2] Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China

年份：2022

外文期刊名：SSRN

收录：EI(收录号：20220121696)

语种：英文

外文关键词：Data mining - Fault detection - Generative adversarial networks

摘要：Data based intelligent fault diagnosis method is an important tool for ensuring the stability of industrial process. However, in the actual industrial process, due to the difficulty of feature extraction and the lack of labeled fault data, it is hard to form a fault diagnosis model with good performance. To address this problem, the Self-Attention embedded Generative Adversarial Networks combined with the Residual Network(SAGAN-ResNet) is proposed in this study. Firstly, to handle the lack of the fault data, the data enhancement method which consists of Self-Attention embedded generator and discriminator is adopted. Then, to extract the feature for better diagnosis performance, based on the augmented training dataset, Residual Network (ResNet) is introduced. Finally, the proposed method is compared with others, the results show that proposed method has advantages in the case of complex process fault diagnosis with few shot industrial data. ? 2022, The Authors. All rights reserved.