计算机应用与软件2024，Vol.41Issue(6)：108-114,127,8.DOI:10.3969/j.issn.1000-386x.2024.06.016
基于多尺度时间卷积网络的多模态过程故障诊断方法
FAULT DIAGNOSIS BASED ON MULTISCALE TEMPORAL CONVOLUTIONAL NETWORK FOR MULTIMODE INDUSTRIAL PROCESS
摘要
Abstract
Aimed at the problem of industrial process fault diagnosis with the mixed characteristics of multimode and multiscale,a fault diagnosis method based on multiscale temporal convolutional network is proposed.Considering the multimode distribution characteristics of process data,we used the local neighborhood standardization method based on cosine similarity to standardize the process data to eliminate the multimode characteristics.Aimed at the multiscale characteristics of the process data,the multiscale representation of the process data was obtained by variational mode decomposition,a temporal convolutional network model with attention mechanism was constructed for each component to extract features,and the multiscale features were fused to achieve multiscale feature extraction.On the basis of the feature extraction,the fault diagnosis was realized by a full connection layer.The effectiveness and feasibility of the proposed method are verified by Tennessee-Eastman(TE)process simulation experiments.关键词
故障诊断/多模态过程/时间卷积网络/多尺度特征提取/局部近邻标准化Key words
Fault diagnosis/Multimode process/Temporal convolutional network/Multiscale feature extraction/Local neighborhood standardization分类
计算机与自动化引用本文复制引用
阳少杰,里鹏,李帅,周晓锋..基于多尺度时间卷积网络的多模态过程故障诊断方法[J].计算机应用与软件,2024,41(6):108-114,127,8.基金项目
辽宁省自然科学基金项目(2019-MS-344). （2019-MS-344）
