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Non-linear principal components analysis with application to process fault detection

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

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Abstract

Principal component analysis has been used for the development of process performance monitoring schemes for both continuous and batch industrial processes. However, it is a linear technique and in this respect it is not necessarily the most appropriate methodology for handling industrial problems which exhibit nonlinear behaviour. A nonlinear principal component analysis methodology based upon the input-training neural network is proposed for the development of nonlinear process performance monitoring schemes. Kernel density estimation is then used to define the action and warning limits, and a differential contribution plot is derived which is capable of identifying the potential source of process faults in nonlinear situations. Finally, the methodology is evaluated through the development of a process performance monitoring scheme for an industrial fluidized bed reactor.

Publication metadata

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

Publication type: Article

Publication status: Published

Journal: International Journal of Systems Science

Year: 2000

Volume: 31

Issue: 11

Pages: 1473-1487

ISSN (print): 0020-7721

ISSN (electronic): 1464-5319

Publisher: Taylor & Francis

URL: http://dx.doi.org/10.1080/00207720050197848

DOI: 10.1080/00207720050197848

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