The Principal Component Analysis (PCA) and the Partial Least Squares (PLS) are two commonly used techniques for process monitoring. Both PCA and PLS assume that the data to be analysed are not self-correlated i.e. time-independent. However, most industrial processes are dynamic so that the assumptions of time-independence made by the PCA and the PLS are invalid in nature. Dynamic extensions to PCA and PLS, so called DPCA and DPLS, have been developed to address this problem, however, unsatisfactorily. Nevertheless, the Canonical Variate Analysis (CVA) is a state-space based monitoring tool, hence is more suitable for dynamic monitoring than DPCA and DPLS. The CVA is a linear tool and traditionally for simplicity, the upper control limit (UCL) of monitoring metrics associated with the CVA is derived based on a Gaussian assumption. However, most industrial processes are non-linear and the Gaussian assumption is invalid for such processes so that CVA with a UCL based on this assumption may not be able to correctly identify underlying faults. In this work, a new monitoring technique using the CVA with UCLs derived from the estimated probability density function through kernel density estimations (KDE) is proposed

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.

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