In gas turbine engine condition monitoring systems, diagnostic algorithms based on measured gas path variables constitute an important component. Not only gas path faults are diagnosed by these algorithms, but also malfunctions of sensors and an engine control system can be identified with gas path measurements. Many gas path diagnostic algorithms use pattern classification techniques. In particular, a specific neural network, Multilayer Perceptron (MLP), is mostly applied. Unfortunately, the MLP cannot provide confidence estimation for its diagnostic decisions. However, there are techniques that classify patterns on the basis of probability. For example, Parzen Window and K-Nearest Neighbor methods compute probabilities of the considered classes estimating their probability densities. Thus, every diagnosis made is accompanied by its probability that is a very useful property for real gas turbine diagnosis. In the present paper, these two techniques are compared with the MLP in order to determine the technique that provides the best diagnostic accuracy on average for all possible gas turbine faults. The mentioned advantage of the Parzen Windows and K-Nearest Neighbors is also taken into account. Key words: gas turbine, pattern classification, Multilayer Perceptron, Parzen Windows, K-Nearest Neighbors
