南京理工大学学报（自然科学版）2018，Vol.42Issue(2)：195-203,9.DOI:10.14177/j.cnki.32-1397n.2018.42.02.010
基于多阶段ICA-SVDD的间歇过程故障监测
Fault monitoring for batch process based on multi-stage ICA-SVDD
摘要
Abstract
In view of the characteristics of multi-stages and the non-Gaussian of the batch process,an improved stage division and fault monitoring method is proposed. Firstly,the stage is divided accord-ing to the similarity of each time slice and the k-means algorithm,and then the independent compo-nent analysis(ICA) method is used to extract the feature information of non-Gaussian of each stage respectively. Finally,the support vector data description(SVDD) algorithm is introduced to establish a statistical analysis model for the independent components and the remaining Gaussian residual spaces,and the whole process is monitored. The feasibility and effectiveness of the proposed method is verified by an actual fault monitoring application for the semiconductor etch process.关键词
间歇过程/阶段划分/独立成分分析/支持向量数据描述/故障监测Key words
batch processes/stage division/independent component analysis/support vector data description/fault monitoring分类
信息技术与安全科学引用本文复制引用
郑皓,熊伟丽..基于多阶段ICA-SVDD的间歇过程故障监测[J].南京理工大学学报（自然科学版）,2018,42(2):195-203,9.基金项目
国家自然科学基金(61773182) （61773182）
