To solve the problem of incipient fault diagnosis of rolling bearing of aero-engine based on the testing signal from engine case

a fusion fault diagnosis approach based on regularized multiple kernel discriminant analysis was proposed. In this method

firstly

several different types of features for the fault diagnosis of rolling bearing are extracted. Secondly

for each of these types of features

a group of kernel matrices are computed by the same set of kernel parameters respectively

then all of the kernel matrices are combined together. Finally

the optimal linear combination coefficients of the kernel matrices for the objective function of regularized kernel discriminant analysis are obtained by solving a semi-infinite linear program

then the linear combination of the kernel matrices was obtained by the combination coefficients to fuse the information of different types of features. The experimental results demonstrate that the proposed fusion fault diagnosis method can improve the accuracy of fault diagnosis about 9.25% significantly when compared with the diagnosis method using a single type of features

and can also improve the level of automation of fault diagnosis by avoiding the problem that kernel matrix must be selected manually.