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.
