Detecting and isolating multiple plant-wide oscillations via spectral independent component analysis

• Published in: Automatica (Oxford) Vol. 41; no. 12; pp. 2067 - 2075
• Main Authors: Xia, Chunming, Howell, John, Thornhill, Nina F.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.12.2005; Elsevier
• Subjects: Applied sciences; Chemical industry; Computer science; control theory; systems; Control theory. Systems; Exact sciences and technology; Fault diagnosis; Independent component analysis; Information, signal and communications theory; Miscellaneous; Multivariate analysis; Oscillation; Plant-wide disturbance; Power spectrum; Principal component analysis; Process control; Process control. Computer integrated manufacturing; Signal and communications theory; Signal processing; Signal, noise; Spectral analysis; Telecommunications and information theory; Fault diagnosis; Chemical industry; Plant-wide disturbance; Process control; Independent component analysis; Oscillation; Multivariate analysis; Spectral analysis; Power spectrum; Principal component analysis; Measurement; Disturbance rejection; Oscillation frequency; Fourier analysis; Perturbation; Narrow band; Multiresolution analysis; Surveillance; Automatic measurement; Multiplicity; Fault diagnostic
• ISSN: 0005-1098; 1873-2836
• DOI: 10.1016/j.automatica.2005.02.011

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.