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

  • Authors:

  • Chunming Xia;John Howell;Nina F. Thornhill

  • Affiliations:

  • Center for Mechatronics Engineering, East China University of Science & Technology, Shanghai 200237, China;Department of Mechanical Engineering, University of Glasgow, Glasgow G12 8QQ, Scotland, UK;Department of Electronic and Electrical Engineering, University College London, Torrington Place, London WC1E 7JE, UK

  • Venue:
  • Automatica (Journal of IFAC)
  • Year:
  • 2005

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Abstract

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.