Gas turbine fault diagnosis using probabilistic neural networks

Efficiency of gas turbine monitoring systems primarily depends on the accuracy of employed algorithms, in particular, pattern classification techniques for diagnosing gas path faults. In recent investigations many techniques have been applied to classify gas path faults, but recommendations for selecting the best technique for real monitoring systems are still insufficient and often contradictory. In our previous work, three classification 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 classification technique, Probabilistic Neural Network (PNN), and we compare it with the techniques previously examined. The results for all comparison cases show that the PNN is not inferior to the other techniques. We recommend choosing the PNN for real monitoring systems because it has an important advantage of providing confidence estimation for every diagnostic decision made.