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

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Rahmoune M. B. , Hafaifa A. , Abdellah K. , Chen X. Q.

tom Vol. 18, No. 4

3--10

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.

Faults detection in gas turbine using hybrid adaptive network based fuzzy inference systems

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Hadroug N. , Hafaifa A. , Kouzou A. , Chaibet A.

tom Vol. 17, No. 4

3--17

The main aim of the present paper is the implementation of a fault detection strategy to ensure the fault detection in a gas turbine which is presenting a complex system. This strategy is based on an adaptive hybrid neuro fuzzy inference technique which combines the advantages of both techniques of neuron networks and fuzzy logic, where, the objective is to maintain the desired performance of the studied gas turbine system in the presence of faults. On the other side, the representation of fuzzy knowledge in the learning neural networks has to be accurate to provide significant improvements for modeling of the studied system dynamic behavior. The results presented in this paper proves clearly that the proposed detection technique allows the perfect detection of the studied gas turbine malfunctions, furthermore it shows that the use of the proposed technique based on the Adaptive Neuro-Fuzzy Interference System (ANFIS) approach which uses the adaptive learning mechanism of neuron networks and fuzzy inference techniques, can be a promising technique to be applied in several industrial application for faults detection.

New approach for prediction the dc breakdown voltage using fuzzy logic controller

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Rouini A. , Derbel N. , Hafaifa A. , Kouzou A.

tom Vol. 19, No. 3

55--62

High voltage device design needs predicting the withstanding voltage to assay conditions as pulses, surges and the DC voltage. There is a great designer needed to have reliable design requirements and welldefined simulation procedure for the development of the apparatus. In this paper, Fuzzy Logic (FL) method is used to model breakdown voltage, based on experimental data generated in the laboratory. Different models are proposed with different membership functions for the FL under both DC voltage conditions. The purpose of this article is to investigate the discharge phenomenon for an air gap-point plan at with insulation barrier between themselves. Obtained results are encouraging. Proving that fuzzy logic is a powerful tool that can be used in predicting the properties of the barrier.