Plant-wide detection and diagnosis using correspondence analysis

This paper presents an approach based on the correspondence analysis (CA) for the task of fault detection and diagnosis. Unlike other data-based monitoring tools, such as principal components analysis/dynamic PCA (PCA/DPCA), the CA algorithm has been 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 here to yield superior performance from the viewpoints of data compression, discrimination and classification, as well as early detection and diagnosis of faults. Metrics similar to the contribution plots and threshold statistics that have been developed and used for PCA are also proposed in this paper for detection and diagnosis using the CA algorithm. Further, using the benchmark Tennessee Eastman problem as a case study, significant performance improvements are demonstrated in monitoring and diagnosis (in terms of shorter detection delays, smaller false alarm rates, reduced missed detection rates and clearer diagnosis) using the CA algorithm over those achievable using the PCA and DPCA algorithms.