Abstract: Aeroengine is a very complex nonlinear system, which brings a big challenge for its fault diagnosis. In the past decade, intelligent techniques have been utilized, such as self-organizing competitive neural network and BP neural network, to solve the problem. These two techniques have their own advantages and disadvantages. It seems that the best diagnosis result cannot be got with only one of the two techniques. In this paper, Dempster-Shafter theory is used to build a new diagnosis system by blending and tuning the output of self-organizing competitive neural network based diagnosis sub-system and that of BP neural network based sub-system. Test results show that the system can diagnose and detect the faults of aeroengine gas path with more precision and efficiency than either single sub-system. In addition, this new D-S theory and two neural networks based diagnosis system can be used to obtain better ability of rejecting noise than the two sub-systems, with adjusting the weights of the diagnosis decisions of two sub-systems.

Key words: D-S theory, neural network, aeroengine, fault diagnosis, gas path