Walaa H.  Elashmawi, Nesma  A. Kotp, Ghada  El Tawel, 
    A Novel Proposed Neural Network MAD (Monitoring, Analysis and Diagnose) Model for Industrial Gas Turbine,
    International Journal of Soft Computing,
    Volume 13,Issue 3,
    2018,
    Pages 92-101,
    ISSN 1816-9503,
    ijscomp.2018.92.101,
    (https://makhillpublications.co/view-article.php?doi=ijscomp.2018.92.101)
    Abstract: Monitoring, analyzing and diagnostic faults of industrial gas turbine are not an easy way by using
conventional methods to complexity of faults. Artificial neural network is deem an active tool to analysis and
diagnose faults. Here, we suggest an efficient neural network model due to monitor, analysis and diagnose
faults of the gas turbine engine for on-line treatment with a twofold advantage. First, the model is able to
diagnose the fault in case of uncertainty or corrupted data by using semi-intelligent artificial neural network.
Second, it can predict the extent of the deterioration of the performance efficiency of the turbine engine by
using intelligent artificial neural network through a simple GUI. 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 with 10-10 Mean Square Error (MSE) that considered optimal
results when compared with training data sets.
    Keywords: Gas turbine;artificial neural network;fault diagnosis system;graphical user interface;fault monitoring system;fault analyzing system