A modified SOM method based on nonlinear neural weight updating for bearing fault identification in variable speed condition

Fault identification for bearings of special electromechanical equipment is significant to avoid catastrophic accidents. However, spectral aliasing and nonstationarity resulted from variable speed condition make this task difficult. In this paper, a modified self-organizing maps (SOM) based on nonlinear neural weight updating way is proposed to solve the problem of bearing fault severity identification in variable speed condition. Firstly, a multi-domain features extraction method based on angular re-sampling technique is introduced. Then considering the nonlinear relationship between fault severity and fault features, the traditional Euclidian distance of SOM is substituted with the geodesic distance when update the neural weight and select the best-matching cell, which can improve the nonlinear identification ability of proposed method. Finally, two cases are performed and the results show that the method can identify bearing fault with different severities effectively and have practical significance when considering both accuracy and time cost compared with other methods.