Title :
Early fault detection and isolation in coal mills based on self-organizing maps
Author :
Aleksandar Ž. Rakić
Author_Institution :
University of Belgrade, School of Electrical Engineering, Belgrade 11020, Serbia
Abstract :
Classical approaches to the fault detection and isolation usually require extensive plant-modeling and statistical analysis of the measured signals and their residuals versus the developed model. In this paper, alternative simple model-free approach is proposed. Real-time data are preprocessed and self-organizing map is trained and used for the reliable isolation of the most frequent mill fault — output fuel-mixture drop due to the coal-stuck in the input bunker. Proposed approach is successfully verified on the real-time data-sets from the coal mills in thermal power plant “Nikola Tesla B”, Serbia.
Keywords :
"Fault detection","Neurons","Real time systems","Training","Temperature","Power generation","Delay"
Conference_Titel :
Neural Network Applications in Electrical Engineering (NEUREL), 2010 10th Symposium on
Print_ISBN :
978-1-4244-8821-6
DOI :
10.1109/NEUREL.2010.5644054