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The present chapter addresses the problems of gas turbine gas path diagnostics solved using artificial neural networks. As a very complex and expensive mechanical system, a gas turbine should be effectively monitored and diagnosed. Being universal and powerful approximation and classification techniques, neural networks have become widespread in gas turbine health monitoring over the past few years. Applications of such networks as a multilayer perceptron, radial basis network, probabilistic neural network, and support vector network were reported. However, there is a lack of manuals that summarize neural network applications for gas turbine diagnosis. A monitoring system comprises many elements, and many factors influence the final diagnostic accuracy. The present chapter generalizes our investigations that are devoted to the enhancement of this system by choosing the best option for each element. In these investigations, a diagnostic process is simulated on the basis of neural networks, and we focus on reaching the highest accuracy by choosing the best network and its optimal tuning to the issue to solve. Thus, helping with enhancement of a whole monitoring system, neural networks themselves are objects of investigation and optimization. As a result of the conducted investigations, the chapter provides the recommendations on choosing and tailoring the network for a particular diagnostic task.

Neural Networks for Gas Turbine Diagnosis