Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/74463
DC FieldValue
dc.titleA self-organizing map approach for process fault diagnosis during process transitions
dc.contributor.authorNg, Y.S.
dc.contributor.authorSrinivasan, R.
dc.date.accessioned2014-06-19T06:12:44Z
dc.date.available2014-06-19T06:12:44Z
dc.date.issued2004
dc.identifier.citationNg, Y.S.,Srinivasan, R. (2004). A self-organizing map approach for process fault diagnosis during process transitions. AIChE Annual Meeting, Conference Proceedings : 7709-7720. ScholarBank@NUS Repository.
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/74463
dc.description.abstractIn this paper, we outline a self-organizing map (SOM) based approach to monitor process transitions. The framework integrates SOM with clustering and sequence comparison methods for plant wide monitoring and fault diagnosis. Process abnormality is detected through cluster analysis while syntactic pattern recognition technique and profile sequence comparison techniques render data based fault diagnosis and machine learning possible. Furthermore, the proposed method also inherits the powerful visualization facility of SOM. Extensive testing on the operations of a lab-scale distillation column illustrates the method's efficacy.
dc.sourceScopus
dc.subjectCluster analysis
dc.subjectFault diagnosis
dc.subjectMonitoring
dc.subjectSelf-organizing map
dc.typeConference Paper
dc.contributor.departmentCHEMICAL & BIOMOLECULAR ENGINEERING
dc.description.sourcetitleAIChE Annual Meeting, Conference Proceedings
dc.description.page7709-7720
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Staff Publications

Show simple item record
Files in This Item:
There are no files associated with this item.

Page view(s)

77
checked on Jan 26, 2023

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.