Aeroengine is a very complex nonlinear object.Traditional methods for its fault diagnosis were proved time-consuming and low efficient.A new system based on rough sets and neural networks for the fault diagnosis of aeroengine gas path component faults was presented in this paper.At first

the rough set theory was used to detect qualitatively faults and to isolate the fault.It consists of three steps:performing the discretion of sensed data

establishing the decision table and generating rules.After that

feed-forward neural networks are added into the system to construct several subsystems

which take the engine sensible data pretreated by rough sets as inputs and compute damage degrees of the aeroengine fault.Finally

the noise rejection abilities of the engine fault diagnosis system were analyzed.The test results show that the system can quantitatively diagnose the faults of aeroengine gas path components precisely and efficiently

while it is robust for noise rejection.