Abstract
An intelligent process monitoring and fault diagnosis environment has been developed by interfacing multivariate statistical process monitoring (MSPM) techniques and knowledge-based systems (KBS) for monitoring multivariable process operation. The real-time KBS developed in G2 is used with multivariate SPM methods based on canonical variate state space (CVSS) process models. Fault detection is based on T2 charts of state variables, contribution plots in G2 are used for determining the process variables that have contributed to the out-of-control signal indicated by large T2 values, and G2 Diagnostic Assistant (GDA) is used to diagnose the source causes of abnormal process behavior. The MSPM modules developed in Matlab are linked with G2. This setup extends the statistical process control library of GDA significantly and permits the use of MSPM tools for autocorrelated data and multivariable processes. The structure of the integrated system is described and its performance is illustrated by simulation studies.
Published Version
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