Simultaneous on-line monitoring and wave-net learning

Current on-line wave-net learning algorithm adapts the primary identified process model with the new changes in time varying processes without a consideration of abnormal situations in the process operation. Therefore, if a disturbance occurs and makes changes in the process, current on-line learnin...

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Bibliographic Details
Main Authors: Jafari, Masoumeh, Safavi, Ali Akbar
Format: Conference Proceeding
Language:English
Subjects:
component
Computerized monitoring
Continuous-stirred tank reactor
CSTR
DPCA
Inductors
Multiresolution analysis
Neural networks
On-line learning
On-line monitoring
Power engineering and energy
Power engineering computing
Principal component analysis
Statistical analysis
Wave-Nets
Wavelet analysis
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Description
Summary:Current on-line wave-net learning algorithm adapts the primary identified process model with the new changes in time varying processes without a consideration of abnormal situations in the process operation. Therefore, if a disturbance occurs and makes changes in the process, current on-line learning updates the primary model to an unsuitable model. This paper proposes a procedure that first determines normal variations of time-varying processes from abnormal variations incorporating an adaptive dynamic principal component analysis (Adaptive DPCA) and updates the model only based on normal variations. A double continuously stirred tank reactors (CSTR) case study is invoked to show the effectiveness of the proposed approach. The results show the effectiveness of the method.
ISSN:2164-7054
DOI:10.1109/IRANIANCEE.2010.5506984