LAUSR

Abstract Variational mode decomposition (VMD) method is a recently employed signal processing technique for fault identification from the vibration signal of rolling bearings. However, the selection of VMD parameters namely the number of modes (k) and the quadratic penalty factor (α) still represents a challenge to obtain proper decomposition modes with the most relevant and denoised fault information. This paper presents a framework using sailfish optimization (SFO) algorithm and Gini index (GI) as a criterion to adaptively select the optimum VMD parameters for each fault signal. The proposed algorithm is tested using three experimental signals of faulty bearings, and the most appropriate mode containing fault information is automatically extracted based on maximum GI values. The obtained results indicate high efficiency of the proposed method in extracting fault feature and in exclusion of noise effect as compared to conventional fixed-parameter VMD, local mean decomposition (LMD), and ensemble empirical mode decomposition (EEMD).