A novel collaborative diagnosis approach of incipient faults based on VMD and SCN for rolling bearing

Abstract In order to overcome the shortcomings of concealed early fault features of rolling bearings which can not be better recognized and the accuracy of early fault diagnosis is not high enough, a novel collaborative diagnosis method is presented combing with variational modal decomposition (VMD) and stochastic configuration network (SCN) for incipient faults of rolling bearing. First, decomposing the original signal by VMD, and then extracting peak‐to‐peak of intrinsic mode function component from each fault, and calculating sample entropy of peak‐to‐peak to construct characteristic sample of fault. Second, based on VMD composition, proposing an incipient fault diagnosis method which is named SCN. Finally, compared with other classification methods, the results show that the proposed collaborative method is effective and advantageous.