Development of Hybrid Intelligent Systems for Boiler Fault Detection and Diagnosis
correct and timely detection is one of major importance in the field of system engineering, and constitutes a primary problem in a fault broad spectrum of case, from industrial processes to high-performance systems and mass produced consumer equipment. A large number of methods can be found in the l...
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my.utp.eprints.42022017-01-19T08:25:27Z Development of Hybrid Intelligent Systems for Boiler Fault Detection and Diagnosis Ismail , F.B. Al-Kayiem, Hussain H. TJ Mechanical engineering and machinery correct and timely detection is one of major importance in the field of system engineering, and constitutes a primary problem in a fault broad spectrum of case, from industrial processes to high-performance systems and mass produced consumer equipment. A large number of methods can be found in the literature, but the recent use of radial basis function neural networks and genetic algorithms for solving fault-diagnosis problem in real industrial situations seems to be particularly promising. A real system (3 boilers of a 3*700 MW thermal power plant) has been chosen to test the hybrid intelligent systems under construction in the present work. As a result, this hybrid approach makes the neural network smaller in size and higher in generalization ability. Keywords: Radial Basis Function Neural Networks (RBFNN), Genetic Algorithms (GA), Fault Detection and Diagnosis (FDD), Thermal Power Plant (TPP) 2009-03-25 Conference or Workshop Item PeerReviewed application/pdf http://eprints.utp.edu.my/4202/1/NPC2009-firas_1_page.pdf Ismail , F.B. and Al-Kayiem, Hussain H. (2009) Development of Hybrid Intelligent Systems for Boiler Fault Detection and Diagnosis. In: 1st National Post Graduate Conference, NPC09, 25-26 March 2009, Universiti Teknologi PETRONAS, Malaysia. http://eprints.utp.edu.my/4202/ |
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TJ Mechanical engineering and machinery Ismail , F.B. Al-Kayiem, Hussain H. Development of Hybrid Intelligent Systems for Boiler Fault Detection and Diagnosis |
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correct and timely detection is one of major importance in the field of system engineering, and constitutes a primary problem in a fault broad spectrum of case, from industrial processes to high-performance systems and mass produced consumer equipment. A large number of methods can be found in the literature, but the recent use of radial basis function neural networks and genetic algorithms for solving fault-diagnosis problem in real industrial situations seems to be particularly promising. A real system (3 boilers of a 3*700 MW thermal power plant) has been chosen to test the hybrid intelligent systems under construction in the present work. As a result, this hybrid approach makes the neural network smaller in size and higher in generalization ability.
Keywords: Radial Basis Function Neural Networks (RBFNN), Genetic Algorithms (GA), Fault Detection and Diagnosis (FDD), Thermal Power Plant (TPP)
|
format |
Conference or Workshop Item |
author |
Ismail , F.B. Al-Kayiem, Hussain H. |
author_facet |
Ismail , F.B. Al-Kayiem, Hussain H. |
author_sort |
Ismail , F.B. |
title |
Development of Hybrid Intelligent Systems for Boiler Fault Detection and Diagnosis |
title_short |
Development of Hybrid Intelligent Systems for Boiler Fault Detection and Diagnosis |
title_full |
Development of Hybrid Intelligent Systems for Boiler Fault Detection and Diagnosis |
title_fullStr |
Development of Hybrid Intelligent Systems for Boiler Fault Detection and Diagnosis |
title_full_unstemmed |
Development of Hybrid Intelligent Systems for Boiler Fault Detection and Diagnosis |
title_sort |
development of hybrid intelligent systems for boiler fault detection and diagnosis |
publishDate |
2009 |
url |
http://eprints.utp.edu.my/4202/1/NPC2009-firas_1_page.pdf http://eprints.utp.edu.my/4202/ |
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1738655325276340224 |
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13.19449 |