Browsing by Author "Lee, Chia-Lung"

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Applying Neural Networks to Detect the Failures of Turbines in Thermal Power Facilities
(2009)
Chen, Kai-Ying
;
Chen, Long-Sheng
;
Chen, Mu-Chen
;
Lee, Chia-Lung
;
運輸與物流管理系註：原交通所+運管所
Due to the growing demand on electricity, how to improve the efficiency of equipment has become one of the critical issues in a thermal power plant. Related works reported that efficiency and availability depend heavily on high reliability and maintainability. Recently, the concept of e-maintenance has been introduced to reduce the cost of maintenance. In e-maintenance systems, the intelligent fault detection system plays a crucial role for identifying failures. Machine learning techniques are at the core of such intelligent systems and can greatly influence their performance. Applying these techniques to fault detection makes it possible to shorten shutdown maintenance and thus increase the capacity utilization rates of equipment. Therefore, this work applies Back-propagation Neural Networks (BPN) to analyze the failures of turbines in thermal power facilities. Finally, a real case from a thermal power plant is provided to evaluate the effectiveness.
Using SVM based method for equipment fault detection in a thermal power plant
(2011-01-01)
Chen, Kai-Ying
;
Chen, Long-Sheng
;
Chen, Mu-Chen
;
Lee, Chia-Lung
;
運輸與物流管理系註：原交通所+運管所
Due to the growing demand on electricity, how to improve the efficiency of equipment in a thermal power plant has become one of the critical issues. Reports indicate that efficiency and availability are heavily dependant upon high reliability and maintainability. Recently, the concept of e-maintenance has been introduced to reduce the cost of maintenance. In e-maintenance systems, the intelligent fault detection system plays a crucial role for identifying failures. Data mining techniques are at the core of such intelligent systems and can greatly influence their performance. Applying these techniques to fault detection makes it possible to shorten shutdown maintenance and thus increase the capacity utilization rates of equipment. Therefore, this work proposes a support vector machines (SVM) based model which integrates a dimension reduction scheme to analyze the failures of turbines in thermal power facilities. Finally, a real case from a thermal power plant is provided to evaluate the effectiveness of the proposed SVM based model. Experimental results show that SVM outperforms linear discriminant analysis (LDA) and back-propagation neural networks (BPN) in classification performance. (C) 2010 Elsevier B.V. All rights reserved.