Disturbance and fault diagnosis techniques that rely on statistical methods traditionally utilize distance based discrimination functions.... Show moreDisturbance and fault diagnosis techniques that rely on statistical methods traditionally utilize distance based discrimination functions. Complementary information is contained in the angular relations between data clusters representing process operations under various disturbances. A novel disturbance diagnosis approach is presented based on angle discriminants. The diagnosis method is successful in cases where distance based discrimination is not very accurate. The methodology is illustrated by diagnosing various disturbances in the Tennessee Eastman process and compared with the diagnosis utilizing distance based algorithms. Endnote format citation Show less
A new methodology was reported [1,2] for integrated use of principal components analysis (PCA) and discriminant analysis in order to determine... Show moreA new methodology was reported [1,2] for integrated use of principal components analysis (PCA) and discriminant analysis in order to determine out-of-control status of a continuous process and to diagnose the source causes for abnormal behavior. Most of the disturbances were identified with good rates of success, with a higher success rate for step or ramp type of disturbances. Quantitative tools that evaluate overlap and similarity between high-dimensional PCA models are proposed in this communication, and their implications on determining the discrimination power of PCA models of processes operating under disturbances are discussed. Diagnosis of several disturbances occurring simultaneously is also investigated. The criterion developed provide upper limits of discrimination power of various single and multiple process disturbances. The techniques developed are illustrated by assessing the process described by the Tennessee Eastman Control Challenge problem [3]. Endnote format citation Show less
