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Lookup NU author(s): Dr Feng Jia,
Professor Elaine Martin,
Emeritus Professor Julian Morris
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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.
Author(s): Martin EB; Jia F; Morris AJ
Publication type: Article
Publication status: Published
Journal: International Journal of Systems Science
ISSN (print): 0020-7721
ISSN (electronic): 1464-5319
Publisher: Taylor & Francis
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