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Wavelets and non-linear principal components analysis for process monitoring

Lookup NU author(s): Professor Elaine Martin, Emeritus Professor Julian Morris

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Abstract

A non-linear principal component analysis (PCA) algorithm is proposed for process performance monitoring based upon an input-training neural network. Prior to assessing the capabilities of the monitoring scheme on an industrial dryer, the data is first pre-processed to remove noise and spikes through wavelet de-noising. The wavelet coefficients obtained are used as the inputs for the non-linear PCA algorithm. Performance monitoring charts with non-parametric control limits are then applied to identify the occurrence of non-conforming operation prior to interrogating differential contribution plots to help identify the potential source of the fault. Encouraging results were achieved.

Publication metadata

Author(s): Martin EB; Morris AJ; Shao R; Jia F

Publication type: Article

Publication status: Published

Journal: Control Engineering Practice

Year: 1999

Volume: 7

Issue: 7

Pages: 865-879

Print publication date: 01/07/1999

ISSN (print): 0967-0661

ISSN (electronic): 1873-6939

Publisher: Elsevier Science Ltd

URL: http://dx.doi.org/10.1016/S0967-0661(99)00039-8

DOI: 10.1016/S0967-0661(99)00039-8

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