PCA-SDG based process monitoring and fault diagnosis

Significant research has been done in recent years to use principal compone nt analysis (PCA) for process fault diagnosis. The general approach involve s manual interpretation of measured variable contributions to the residual and/or principal components. For a large chemical process, this could be te dious and often impossible. In addition, it hampers the automation of high- level analysis and decision support tasks that require root cause informati on. In this work, the interpretation of PCA-based contributions is automate d using signed digraphs (SDGs). Also, a serious limitation of SDG-based dia gnosis - the assumption of a single fault is overcome by developing a SDG-b ased multiple fault diagnosis algorithm. The implementation of the PCA-SDG- based fault diagnosis algorithms is done using G2. Its application is illus trated on the Amoco Model IV Fluidized Catalytic Cracking Unit (FCCU). (C) 1999 Elsevier Science Ltd. All rights reserved.