PCA-SDG based process monitoring and fault diagnosis

Abstract

Significant research has been done in recent years to use principal component analysis (PCA) for process fault diagnosis. The general approach involves manual interpretation of measured variable contributions to the residual and/or principal components. For a large chemical process, this could be tedious and often impossible. In addition, it hampers the automation of high-level analysis and decision support tasks that require root cause information. In this work, the interpretation of PCA-based contributions is automated using signed digraphs (SDGs). Also, a serious limitation of SDG-based diagnosis – the assumption of a single fault – is overcome by developing a SDG-based multiple fault diagnosis algorithm. The implementation of the PCA-SDG-based fault diagnosis algorithms is done using G2. Its application is illustrated on the Amoco Model IV Fluidized Catalytic Cracking Unit (FCCU).