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

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.

[1]  P. Welch.  The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms , 1967 .

[2]  B. K. Alsberg.  Multiscale cluster analysis. , 1999, Analytical chemistry.

[3]  Nathalie Delfosse,et al.  Adaptive blind separation of independent sources: A deflation approach , 1995, Signal Process..

[4]  Chunming Xia,et al.  Isolating multiple sources of plant-wide oscillations via independent component analysis , 2003 .

[5]  Te-Won Lee,et al.  Independent Component Analysis , 1998, Springer US.

[6]  S. Joe Qin,et al.  Control performance monitoring — a review and assessment , 1998 .

[7]  Christian Jutten,et al.  Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture , 1991, Signal Process..

[8]  Richard M. Everson,et al.  Independent Components Analysis , 2000, Artificial Neural Networks in Biomedicine.

[9]  X. Wang,et al.  Dimension reduction of process dynamic trends using independent component analysis , 2002 .

[10]  Athanasios Papoulis,et al.  Probability, Random Variables and Stochastic Processes , 1965 .

[11]  George Carayannis,et al.  Character generation and scaling through fast transform techniques , 1991, Signal Process..

[12]  ChangKyoo Yoo,et al.  Statistical process monitoring with independent component analysis , 2004 .

[13]  John W. Cox,et al.  A practical approach for large-scale controller performance assessment, diagnosis, and improvement , 2003 .

[14]  Manabu Kano,et al.  Evolution of multivariate statistical process control: application of independent component analysis and external analysis , 2004, Comput. Chem. Eng..

[15]  Bhavik R. Bakshi,et al.  Multiscale analysis and modeling using wavelets , 1999 .

[16]  S. Joe Qin,et al.  Multivariate process monitoring and fault diagnosis by multi-scale PCA , 2002 .

[17]  Michael J. Piovoso,et al.  PCA of Wavelet Transformed Process Data for Monitoring , 1997, Intell. Data Anal..

[18]  Biao Huang,et al.  Spectral principal component analysis of dynamic process data , 2002 .

[19]  W. H. Williams,et al.  Probability Theory and Mathematical Statistics , 1964 .

[20]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[21]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[22]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..