Please use this identifier to cite or link to this item:
|Title:||A self-organizing map approach for process fault diagnosis during process transitions|
|Authors:||Ng, Y.S. |
|Source:||Ng, 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.|
|Abstract:||In 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.|
|Source Title:||AIChE Annual Meeting, Conference Proceedings|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 9, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.