Abstract
Quick detection of out-of-control status and diagnosis of disturbances leading to the abnormal process operation are crucial in minimizing product quality variation. Multivariate statistical techniques are used in developing methodology for detection of abnormal process behavior and diagnosis of disturbances causing poor process performance. The methodology is illustrated by monitoring the Tennessee Eastman plant simulation benchmark problem. Twenty-one different disturbances are introduced to the plant. Most of the disturbances can be diagnosed correctly, the success rate being higher for step and ramp disturbances than noise disturbances.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
