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 multivariate process operation. In particular, the real-time KBS G2 and its Diagnostic Assistant (GDA) tool are used with multivariate SPM methods based on canonical variate state space (CVSS) process models. Fault detection is based on T 2 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 T 2 values, and GDA is used to diagnose the source cause of the abnormal process behavior. The MSPM modules developed in Matlab are linked with G2 and GDA. This setup extends the Statistical Process Control (GSPC) library of GDA significantly and permits the use of MSPM tools for autocorrelated data and multivariable processes. The presentation will focus on the structure and performance of the integrated system. On-line SPM of multivariable processes will be illustrated by simulation studies.
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