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

By Vinay Kariwala, P. E. Odiowei, Yi Cao and T. Chen


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

Topics: 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
Publisher: Elsevier Science B.V., Amsterdam.
Year: 2010
DOI identifier: 10.1016/j.jprocont.2010.07.007
OAI identifier:
Provided by: Cranfield CERES

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