Abstract
Principal component analysis (PCA) has emerged as a useful tool for process monitoring and fault diagnosis. The general approach requires the user to identify the root cause by interpreting the measured variable contributions to the residual and/or principal components. This could be tedious and often impossible for a large process. It also hinders the automation of high level supervisory tasks like choice of corrective actions and their associated costs. In this paper, the interpretation of the PCA-based contributions is automated using signed directed graphs (SDGs). The implementation of the PCA-SDG based fault diagnosis algorithm is done using Gensym's expert system shell G2. Its application is illustrated on the Amoco Model IV Fluidized Catalytic Cracking Unit (FCCU).
Sign-in/Register to access full text options 
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.
