PCA of Wavelet Transformed Process Data for Monitoring

Article type: Research Article

Authors: Kosanovich, Karlene A.^{a; *} | Piovoso, Michael J.^{b; 1}

Affiliations: [a] Department of Chemical Engineering University of South Carolina, Columbia, SC 29208, USA | [b] Great Valley Campus Pennsylvania State University Malvern, PA 19355, USA

Correspondence: [*] Corresponding author. E-mail: kosanoka@sun.che.sc.edu.

Note: [1] E-mail: mjc@magpage.com.

Abstract: Producing a uniform product is important for several reasons such as maintenance of a competitive position, reduction in the number of shutdowns and startups, and the elimination of the sources of variability. Multivariate statistical methods can assist in the identification of process correlations and the development of process monitoring models. This work extends these concepts by demonstrating that the correlations and resulting monitoring models can be improved greatly with the addition of pre-filtering the time signals using a median filter, and time-scale decomposition using a multi-resolution wavelet function. After the data are filtered and decomposed, the multivariate statistical method of principal component analysis (PCA) is used to develop a process monitoring model. Data that was taken from a difficult-to-operate industrial process are used to demonstrate these ideas.

Keywords: Haar wavelet, FIR/median hybrid filter, Principal component analysis, Process monitoring

DOI: 10.3233/IDA-1997-1203

Journal: Intelligent Data Analysis, vol. 1, no. 2, pp. 85-99, 1997