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Lookup NU author(s): Professor Elaine Martin,
Emeritus Professor Julian Morris
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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.
Author(s): Martin EB; Morris AJ; Shao R; Jia F
Publication type: Article
Publication status: Published
Journal: Control Engineering Practice
Print publication date: 01/07/1999
ISSN (print): 0967-0661
ISSN (electronic): 1873-6939
Publisher: Elsevier Science Ltd
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