Abstract:

In this paper, both back-propagation neural networks (BPNN) and probabilistic neural networks (PNN) are applied to qualitative aero-engine prototype fault diagnosis, and the simulated results are compared with each other. The simulated results show that when the measurements do not contain any noise or the noises are comparatively small, the success rates of diagnosis of both BPNN and PNN are quite high; when noises rise, the success rate of PNN is much higher than that of BPNN.

Key words: neural networks, aero-engine, fault diagnosis, unscheduled maintenance