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Diagnosis of process disturbances by statistical distance and angle measures
Title
Diagnosis of process disturbances by statistical distance and angle measures
Creator
Raich, A., Cinar, A.
Date
1997
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Description
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.
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Statistical process monitoring and disturbance diagnosis in multivariable continuous processes
Title
Statistical process monitoring and disturbance diagnosis in multivariable continuous processes
Creator
Raich, A., Cinar, A.
Date
1996-04
Publisher
AMER INST CHEMICAL ENGINEERS
Description
Detecting out-of-control status and diagnosing disturbances leading to the abnormal process operation early are crucial in minimizing product...
Show moreDetecting out-of-control status and diagnosing disturbances leading to the abnormal process operation early are crucial in minimizing product quality variations Multivariate statistical techniques are used to develop detection methodology for abnormal process behavior and diagnosis of disturbances causing poor process performance. Principal components and discriminant analysis ave applied to quantitatively describe and interpret step, ramp and random-variation disturbances. All disturbances require high-dimensional models for accurate description and cannot be discriminated by biplots. Diagnosis of simultaneous multiple faults is addressed by building quantitative measures of overlap between models of single faults and their combinations. These measures are used to identify the existence of secondary disturbances and distinguish their components. The methodology is illustrated by monitoring the Tennessee Eastman plant simulation benchmark problem subjected to different disturbances. Most of the disturbances can be diagnosed correctly, the success rate being higher for step and vamp disturbances than random-variation disturbances.
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Multivariate statistical methods for monitoring continuous processes: Assessment of discrimination power of disturbance models and diagnosis of multiple disturbances
Title
Multivariate statistical methods for monitoring continuous processes: Assessment of discrimination power of disturbance models and diagnosis of multiple disturbances
Creator
Raich, A. C., Cinar, A.
Date
1995-11
Publisher
ELSEVIER SCIENCE BV
Description
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].
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