A branch and bound method for isolation of faulty variables through missing variable analysis

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dc.contributor.author Kariwala, Vinay -
dc.contributor.author Odiowei, P. E. -
dc.contributor.author Cao, Yi -
dc.contributor.author Chen, T. -
dc.date.accessioned 2011-09-08T10:06:55Z
dc.date.available 2011-09-08T10:06:55Z
dc.date.issued 2010-12-31T00:00:00Z -
dc.identifier.issn 0959-1524 -
dc.identifier.uri http://dx.doi.org/10.1016/j.jprocont.2010.07.007 -
dc.identifier.uri http://dspace.lib.cranfield.ac.uk/handle/1826/4783
dc.description.abstract Fault detection and diagnosis is a critical approach to ensure safe and efficient operation of manufacturing and chemical processing plants. Although multivariate statistical process monitoring has received considerable attention, investigation into the diagnosis of the source or cause of the detected process fault has been relatively limited. This is partially due to the difficulty in isolating multiple variables, which jointly contribute to the occurrence of fault, through conventional contribution analysis. In this work, a method based on probabilistic principal component analysis is proposed for fault isolation. Furthermore, a branch and bound method is developed to handle the combinatorial nature of problem involving finding the contributing variables, which are most likely to be responsible for the occurrence of fault. The efficiency of the method proposed is shown through benchmark examples, such as Tennessee Eastman process, and randomly generated cases. en_UK
dc.language.iso en_UK -
dc.publisher Elsevier Science B.V., Amsterdam. en_UK
dc.subject Branch and bound Combinatorial optimization Global optimization Multivariate contribution analysis Multivariate statistical process monitoring Principal component analysis principal component analysis gaussian mixture model feature-selection part i algorithm pca en_UK
dc.title A branch and bound method for isolation of faulty variables through missing variable analysis en_UK
dc.type Article -

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