All-parameter adaptive feature mode decomposition and its application on incipient fault diagnosis of rolling bearings

Aiming at the problem that the incipient fault features of rolling bearings are easily submerged in the multi-component noise signal and difficult to extract effectively. An all-parameter adaptive feature mode decomposition method was proposed. Firstly, the equivalent filter characteristics of the feature mode decomposition (FMD) was studied by numerical simulation experiment based on fractional Gaussian noise. Secondly, the influence of key parameters (the filter length L, the filter number K, and the mode number n) on the filtering property of FMD was analyzed. Then, in order to realize the adaptive decomposition of fault signals, a sparsity index (periodic harmonic energy ratio PHER) which fully considers its cyclostationarity from the perspective of the frequency domain of the fault signal was constructed, and the PHER was used as the fitness function of the golden jackal optimization algorithm to automatically determine the optimal all-parameter combination. Finally, the simulation signal and experimental data were thoroughly analyzed, demonstrating that the proposed method can suppress multi-component noise interference and effectively extract bearing fault feature information. Compared with PAFMD, MPA-VMD and ISGMD, the proposed method has more superior ability to extract incipient fault characteristics of rolling bearings.