Statistical process monitoring and disturbance diagnosis in multivariable continuous processes

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Abstract
Detecting 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 are 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 ramp disturbances than random‐variation disturbances.