In order to distinguish similar faults of aero-engine gas path fault diagnosis and improve the diagnostic accuracy

a fault diagnosis method based on support vector machine(SVM) and synergetic neural network(SNN) was put forward. Firstly

the SVM after being optimized was used to diagnose and classify the faults preliminarily form measured data

and the diagnosis results were analyzed to obtain indistinguishable similar faults

then the SNN was introduced to distinguish similar faults and further determine corresponding fault model

finally this fault model was simulated based on actual data. The experimental results show that the preliminary fault diagnosis accuracy based on SVM is 96%

and after further distinguishing similar faults through SNN

the accuracy is increased to 100%.