Through combination of deep belief network (DBN)

sampling and integration technology

a fault diagnosis model of civil aero-engine based on unbalanced sample driving was proposed. By analyzing the historical flight data of civil aero-engines

the model used DBN to extract the internal features of the performance parameters

then used the sampling technology to equalize the unbalanced samples

and finally adopted integrated technology for fault classification. The model was applied to historical flight data of CFM56-7B series engines. Compared with common fault diagnosis methods

the experimental results showed that the model had higher accuracy of 0.996 and AUC value of 0.948

and can effectively deal with high-dimensional and unbalanced problems of civil aero-engine samples.