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
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. Endnote format citation Show less
