Neural Network Monitoring Model for Industrial Gas Turbine

Walaa H. Elashmawi, Nesma A. Kotp, Ghada El Tawel


Monitoring and diagnostic faults of industrial gas turbine are not an easy way by using conventional methods due to the nature and complexity of faults. Artificial neural network is considered an efficient tool to monitor and diagnose faults. In this paper, we proposed an efficient neural network model to monitor the gas turbine engine for on-line processing with a twofold advantage. First, the model is able to diagnose the fault in case of uncertainty or corrupted data. Second, it can predict the extent of the deterioration of the performance efficiency of the turbine engine through a simple graphical user interface. The experiment has been done on five faulty conditions and the proposed neural network model tested with new dataset. The results have proven that, the proposed model produced satisfactory results with10-10 mean square error that considered optimal results when compared with training data sets.



Artificial neural network, Fault diagnosis system, Fault monitoring system, Gas turbine, Graphical user interface

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