Rolling mill bearings fault diagnosis based on improved multivariate variational mode decomposition and multivariate composite multiscale weighted permutation entropy

The multi-row bearings of rolling mills are subject to axial and radial loads. Due to the complex working conditions, it is difficult to achieve better results in fault diagnosis by analyzing signal directional vibration signals. In order to realize the fault diagnosis of bearings subjected to multiple directional loads, this paper introduces the idea of cooperative processing of multi-sensing signals and proposes a fault feature extraction method of improved multivariate variational mode decomposition (IMVMD) combined with multivariate composite multiscale weighted permutation entropy (MCMWPE). First, reconstruct the signal by variational modal decomposition (VMD). Upgrade VMD to multi-channel decomposition mode by considering the correlation of the multichannel signal and optimizing its parameters. Secondly, propose the method to calculate multi-channel signal entropy. Represent bearing fault features by calculating the MCMWPE of multi-channel reconstructed signals. The method proposed in this paper is validated on experiment rolling mill datasets and actual rolling mill datasets from a factory. Entropy curves and PSO-SVM classification results show that IMVMD-MCMWPE can extract fault features better than other methods.