Sensor Fault Detection in Power Plants
Publication: Journal of Energy Engineering
Volume 135, Issue 4
Abstract
This paper presents a sensor fault detection and diagnosis approach for industrial combustion processes. Clustering algorithms are applied to the measurements of controllable process variables involved in single-input-single-output feedback control loops. Current data points from the process are compared with the clusters to identify sensor faults. Once the measurements of controllable process variables are obtained, a decision-tree algorithm monitors response process variables based on the controllable and noncontrollable process variables as predictors (inputs). Test data and training data residuals generated by the decision-tree algorithm are analyzed with statistical process control limits to identify sensor faults. The proposed approach handles data from temporal processes by periodic updates of the knowledge base. An industrial boiler combustion process is used to test the ideas presented in this paper.
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Acknowledgments
The research presented in this paper has been partially sponsored by funding from the Iowa Energy Center, IEC Grant No. UNSPECIFIED06-04.
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Information & Authors
Information
Published In
Journal of Energy Engineering
Volume 135 • Issue 4 • December 2009
Pages: 127 - 137
Copyright
© 2009 ASCE.
History
Received: Aug 18, 2008
Accepted: Mar 11, 2009
Published online: Nov 13, 2009
Published in print: Dec 2009
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