On the selection of an optimal pattern recognition technique for gas turbine diagnosis

Efficiency of gas turbine monitoring systems primarily depends on the accuracy of employed algorithms, in particular, pattern recognition techniques to diagnose gas path faults. In investigations many techniques were applied to recognize gas path faults, but recommendations on selecting the best technique for real monitoring systems are still insufficient and often contradictory. In our previous works, three recognition techniques were compared under different conditions of gas turbine diagnosis. The comparative analysis has shown that all these techniques yield practically the same accuracy for each comparison case. The present contribution considers a new recognition technique, Probabilistic Neural Network (PNN), comparing it with the techniques previously examined. The results for all comparison cases show that the PNN is not practically inferior to the other techniques. With this inference, the recommendation is to choose the PNN for real monitoring systems because it has an important advantage of providing confidence estimation for every diagnostic decision made.