A Self-organizing Map Approach for Process Fault Diagnosis during Process Transitions
نویسندگان
چکیده
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.
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