Monitoring of high-speed shaft of gas turbine using artificial neural networks: predictive model application
Ahmed Hafaifa 1  
,  
Kouzou Abdellah 3  
,  
Chen XiaoQi 4  
 
 
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1
Applied Automation and Industrial Diagnostics Laboratory, Faculty of Science and Technology, University of Djelfa
2
Applied Automation and Industrial Diagnostics Laboratory, Faculty of Science and Technology, University of Djelfa 17000 DZ, Algeria
3
) Applied Automation and Industrial Diagnostics Laboratory, Faculty of Science and Technology, University of Djelfa 17000 DZ, Algeria
4
Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand.
CORRESPONDING AUTHOR
Ahmed Hafaifa   

Applied Automation and Industrial Diagnostics Laboratory, Faculty of Science and Technology, University of Djelfa, Djelfa 17000 DZ, Algeria., 17000 Dz Djelfa, Algeria
Publish date: 2017-11-08
Submission date: 2017-05-09
Final revision date: 2017-09-24
Acceptance date: 2017-09-25
 
Diagnostyka 2017;18(4):3–10
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ABSTRACT
The automatic engineering known a very rapid progress with the consequent development of numerical methods and computer systems, by the growth of computational capacity. In this context, this work proposes a strategy of predictive control of the high-pressure shaft speed of a gas turbine using artificial neural networks in order to monitor the vibratory behavior of this rotating machine. This approach makes it possible to ensure the stability of this turbine under real conditions and to detect any deviation of their dynamic behavior from the margin of safety. This approach makes it possible to include the control limitations on the turbine variables in the modeling step of the high-speed shaft speed controller.
eISSN:2449-5220
ISSN:1641-6414