Fault identification algorithms based on measured gas path variables constitute an important component of a gas turbine engine condition monitoring system. In addition to gas path faults diagnosis, these algorithms are capable to identify malfunctions of sensors and an engine control system. The fault identification algorithms widely use pattern recognition techniques, in particular, different artificial neural networks. Since monitoring system efficiency depends on accuracy of all system's components, the most exact mathematical technique should be chosen for every component. To recognize gas turbine faults, a specific network type, multilayer perceptron (MLP), is mostly applied. However, other network type, probabilistic neural network (PNN), can be applied as well. It uses a probabilistic measure to recognize the faults. In the present paper, the PNN is firstly tailored to a gas turbine diagnosis application and then compared with the MLP. The comparison has shown that both networks yield practically equal accuracy. The PNN is recommended for real gas turbine monitoring systems because, in addition to a diagnostic decision, this network provides confidence estimation for this decision.
