Combining conceptual clustering and principal component analysis for statespace based process monitoring

Multivariate statistics and unsupervised machine learning have recently bee n studied by many researchers for process monitoring and fault diagnosis. T hese approaches often depend on calculating a similarity or distance measur e to group data sets into clusters. Apart from giving predictions, they are not able to give causal explanations on why a specific set of data is assi gned to a particular cluster. In this work, a conceptual clustering approac h is presented for designing state space based monitoring systems, which is able to generate conceptual knowledge on the major variables which are res ponsible for clustering, as well as projecting the operation to a specific operational state. A critical issue in this approach is how to conceptually represent dynamic trend signals. For this purpose, principal component ana lysis is used for concept extraction from real-time dynamic trend signals. The method is introduced using a continuous stirred tank reactor as a case study. Application of the approach to a refinery methyl tert-butyl ether pr ocess is also presented.