Fault Detection and Diagnosis for Large -Scale Systems

Implementing an effective process monitoring algorithm is essential in minimizing downtime, increasing the safety of plant operations, and reducing manufacturing costs. Data-driven techniques based on multivariate statistics such as principal component analysis and partial least squares have been applied in many industrial processes and their effectiveness for fault detection is well-recognized. There is an inherent limitation on the ability for data-driven techniques to identify and diagnose faults, especially when the abnormal situations are associated with unknown faults and multiple faults. To improve the proficiency of data-driven techniques for fault identification and diagnosis, algorithms based on Fisher discriminant analysis and principal component analysis are proposed. In addition, a technique which integrates a causal map and data-driven techniques is proposed. The proficiencies of the methods are tested by application to the Tennessee Eastman process simulator and the results indicate that the new measures are better for monitoring the process compared to the existing measures.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Chemical Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Chiang, Leo Hao-Tien

Contributors dc:contributor

• Braatz, Richard D.

• Engineering, Industrial

Language dc:language

eng

Identifier

(MiAaPQ)AAI3023031

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/82332