Bearing fault damage degree identification method based on SSA-VMD and Shannon entropy–exponential entropy decision

Aiming at the problem that the weak fault signal of rolling bearing is affected by background noise and the weak fault signal itself leads to the difficulty in extracting fault features, a weak fault diagnosis method of rolling bearing based on sparrow search algorithm-variational mode decomposition (SSA-VMD) and Shannon entropy–exponential entropy decision is proposed. Firstly, the failure energy ratio of the original signal is acquired to judge the bearing failure. Secondly, the original time-domain signal is decomposed by the VMD optimized by SSA-VMD to obtain the Intrinsic Mode Function (IMF) component, and the kurtosis and correlation coefficient are normalized and fused. The fusion parameter ratio ( R V ) is used to filter the IMF component, and the filtered component is reconstructed to achieve the noise reduction effect. The reconstructed signal is subjected to Hilbert transform to obtain the envelope spectrum of the vibration signal, and the fault type of the bearing can be judged. Finally, the entropy of the reconstructed signal is input into the model based on entropy-multilayer forward neural network (MFNN) to identify the degree of bearing fault damage. The effectiveness of the method is verified by using the experimental data of different fault types of intermediate shaft bearings in Shenyang Aerospace University and the self-built experimental data of outer ring fault detachment evolution. The results show that the fault energy ratio of the original signal is more conducive to judging whether the bearing has a fault than the reconstructed signal. The bearing fault type diagnosis method based on SSA-VMD and parameter fusion screening can effectively identify fault characteristic frequency and its frequency doubling of the inner and outer rings of rolling bearings. The entropy values of different bearing damage signals have different distribution regions, which verify the effectiveness of the bearing fault damage identification method based on entropy–MLP judgement.