FAULT DETECTION USING CORRESPONDENCE ANALYSIS: APPLICATION TO TENNESSEE EASTMAN CHALLENGE PROBLEM

Abstract

This paper presents an approach based on the use of correspondence analysis (CA) for the task of fault detection and diagnosis. Unlike other tools (PCA / DPCA) that are used for this latter task, CA is shown to use a different metric to represent the information content in the data matrix X. Decomposition of the information represented in the metric is shown to yield superior performance from the viewpoints of data compression, discrimination and classification as well as early detection of faults. We demonstrate these performance improvements over PCA and DPCA on the Tennessee Eastman problem, which is a representative benchmark problem used in the literature. CA is shown to yield vastly superior performance for the monitoring of the TE problem, when compared with PCA and DPCA.