文献类型：期刊文献

英文题名：Detecting and isolating multiple plant-wide oscillations via spectral independent component analysis

作者：Xia, Chunming[1]; Howell, John[2]; Thornhill, Nina F.[3,4,5]

机构：[1]Univ Glasgow, Dept Mech Engn, Glasgow G12 8QQ, Lanark, Scotland;[2]E China Univ Sci & Technol, Ctr Mech Engn, Shanghai 200237, Peoples R China;[3]UCL, Dept Elect & Elect Engn, London WC1E 7JE, England

年份：2005

卷号：41

期号：12

起止页码：2067

外文期刊名：AUTOMATICA

收录：;EI(收录号：2005459457808);WOS:【SCI-EXPANDED(收录号:WOS:000233227900005)】；

语种：英文

外文关键词：chemical industry; fault diagnosis; independent component analysis; multivariate analysis; oscillation; plant-wide disturbance; power spectrum; principal component analysis; process control; spectral analysis

摘要：Disturbances that propagate throughout a plant can have an impact on product quality and running costs. There is thus a motivation for the automated detection of plant-wide disturbances and for the isolation of the sources. A new application of independent component analysis (ICA), multi-resolution spectral ICA, is proposed to detect and isolate the sources of multiple oscillations in a chemical process. Its key feature is that it extracts dominant spectrum-like independent components each of which has a narrow-band peak that captures the behaviour of one of the oscillation sources. Additionally, a significance index is presented that links the sources to specific plant measurements in order to facilitate the isolation of the sources of the oscillations. A case study is presented that demonstrates the ability of spectral ICA to detect and isolate multiple dominant oscillations in different frequency ranges in a large data set from an industrial chemical process. (c) 2005 Elsevier Ltd. All rights reserved.