Abstract
Significant research has been done in the past 30 years to use signed directed graph (SDG) for process fault diagnosis. However, due to non-unified SDG models for control loops, highly complex and integrated nature of chemical processes, few of SDG based methods has been applied in the real chemical processes. In this paper, SDG based deep knowledge modeling and bidirectional inference algorithms are introduced. With the algorithms a SDG based fault diagnosis and decision support system is developed and applied in fault diagnosis for an atmospheric distillation unit of a large-scale refining plant in China. The results prove that the SDG based fault diagnosis and decision support system can not only arrive at the fundamental requirement of diagnosis: correctness, completeness and real-timed, but also provide decision support for operators to decrease the possibility of unscheduled shut-down or more serious accident due to abnormal situation.
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.
