A rolling bearing fault diagnosis technique based on fined-grained multi-scale symbolic entropy and whale optimization algorithm-MSVM

Abstract As a critical and fragile rotary supporting component in mechanical equipment, fault diagnosis of rolling bearing has been a hot issue. A rolling bearing fault diagnosis technique based on fined-grained multi-scale symbolic entropy and whale optimization algorithm-multiclass support vector machine (abbreviated as FGMSE-WOA-MSVM) is proposed in this paper. Firstly, the vibration signals are decomposed with fine-grained multi-scale decomposition, and the symbolic entropy of the sub-signals at different analysis scales are extracted and constructed as the multi-dimension fault feature vector. In order to address the problem of sensitive parameters for MSVM model, whale optimization algorithm (abbreviated as WOA) is introduced to optimize the penalty factor and kernel function parameters to construct the optimal WOA-MSVM model. Finally, Instance analysis is carried out with bearing fault dataset from Jiangnan University to verify the parameters influence and the effectiveness on the unbalanced sample set. The results show that compared with different feature vector inputs and learning models such as k-Nearest Neighbor (abbreviated as KNN), Decision Tree (abbreviated as DT), Random Forest (RF), etc., the proposed technique can achieve an accuracy rate of 99.33%, besides, the computation speed is fast and the diagnosis efficiency is high which means its potential value for engineering application.