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Development of Hybrid Intelligent Systems for Boiler Fault Detection and Diagnosis

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.

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Abstract

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)

Item Type:Conference or Workshop Item (Paper)
Subjects:T Technology > TJ Mechanical engineering and machinery
Departments / MOR / COE:Departments > Mechanical Engineering
ID Code:4202
Deposited By: Assoc Prof Hussain H. Al-Kayiem
Deposited On:10 Jan 2012 00:12
Last Modified:19 Jan 2017 08:25

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