Monitoring of high-speed shaft of gas turbine using artificial neural networks: predictive model application

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Journal Article
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Date
2017
Authors
Rahmoune MB
Hafaifai A
Abdellah K
Chen XQ
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.

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Keywords
Monitoring, Gas turbine, Vibrations, Artificial neural networks, Predictive model
Ngā upoko tukutuku/Māori subject headings
ANZSRC fields of research
Field of Research::09 - Engineering::0913 - Mechanical Engineering::091302 - Automation and Control Engineering
Field of Research::08 - Information and Computing Sciences::0801 - Artificial Intelligence and Image Processing::080108 - Neural, Evolutionary and Fuzzy Computation
Fields of Research::40 - Engineering::4017 - Mechanical engineering::401702 - Dynamics, vibration and vibration control
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