Rolling Bearing Fault Diagnosis Based on WOA-VMD-MPE and MPSO-LSSVM

• Published in: Entropy (Basel, Switzerland) Vol. 24; no. 7; p. 927
• Main Authors: Jin, Zhihao, Chen, Guangdong, Yang, Zhengxin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 03.07.2022; MDPI
• Subjects: Accuracy; Algorithms; Bearings; Correlation coefficients; Decomposition; Failure; Fault diagnosis; Feature extraction; K-fold cross-validation; Mathematical models; multi-scale permutation entropy; Optimization; Parameters; Particle swarm optimization; Pattern recognition; Pearson correlation coefficient; Permutations; Roller bearings; rolling bearing; Support vector machines; variational mode decomposition; whale algorithm
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e24070927

In order to further improve the accuracy of fault identification of rolling bearings, a fault diagnosis method based on the modified particle swarm optimization (MPSO) algorithm optimized least square support vector machine (LSSVM), combining parameter optimization variational mode decomposition (VMD) and multi-scale permutation entropy (MPE), was proposed. Firstly, to solve the problem of insufficient decomposition and mode mixing caused by the improper selection of mode component K and penalty factor α in VMD algorithm, the whale optimization algorithm (WOA) was used to optimize the penalty factor and mode component number in the VMD algorithm, and the optimal parameter combination (K, α) was obtained. Secondly, the optimal parameter combination (K, α) was used for the VMD of the rolling bearing vibration signal to obtain several intrinsic mode functions (IMFs). According to the Pearson correlation coefficient (PCC) criterion, the optimal IMF component was selected, and its optimal multi-scale permutation entropy was calculated to form the feature set. Finally, K-fold cross-validation was used to train the MPSO-LSSVM model, and the test set was input into the trained model for identification. The experimental results show that compared with PSO-SVM, LSSVM, and PSO-LSSVM, the MPSO-LSSVM fault diagnosis model has higher recognition accuracy. At the same time, compared with VMD-SE, VMD-MPE, and PSO-VMD-MPE, WOA-VMD-MPE can extract more accurate features.