Rolling Bearing Fault Diagnosis Based on VMD-MTSO-XGBoost at Wide Sampling Frequency

In order to solve the problems of low speed and low accuracy of rolling bearing fault diagnosis caused by the inconsistent sampling frequency of the accelerometer of mechanical vibration monitoring system, a rolling bearing fault diagnosis method based on variational mode decomposition-multi-strategy tuna swarm optimization-extreme gradient boosting (VMD-MTSO-XGBoost) at wide sampling frequency was proposed. Firstly, the vibration signal was de-noised by wavelet and de-sampled to get the de-noised signal at wide sampling frequency. The de-noised signal at wide sampling frequency was processed by variational mode decomposition (VMD), and the intrinsic mode function (IMF) component index was extracted to form the fault feature vector. Secondly, the Circle chaotic map was used to initialize the tuna swarm optimization (TSO) population to increase the richness and diversity of the initial population. In order to improve the ability of the algorithm to jump out of the local optimum and enhance the ability of the algorithm to explore the whole world, the dimension-by-dimension mutation method was used to disturb the optimal individual position. Finally, the modified tuna swarm optimization (MTSO) algorithm was used to optimize the parameters of extreme gradient boosting (XGBoost), and the rolling bearing fault diagnosis model was established. The proposed fault diagnosis method was validated by the Case Western Reserve University dataset, the German University of Paderborn dataset and the measured dataset. Results show that at wide sampling frequency, the fault diagnosis method presented in this paper can identify rolling bearing faults more efficiently and accurately compared with the other three models.