Fault Diagnosis of Rolling Bearing Based on Improved DenseNet Model
Rolling bearing is a key component of mechanical equipment.In order to detect the normal operation of rolling bearing equipment and improve the accuracy of identifying bearing faults,a fault diagnosis model method based on optimized variational mode decomposition(VMD)and improved dense neural network(DenseNet)is proposed.Firstly,multi-population differential evolution(MPDE)algorithm is used to search the parameters of VMD method with local minimum envelope entropy as the objective function to obtain the best parameter combination.Then,the original bearing fault signals are decomposed by using the VMD method of optimal pa-rameter combination optimization,and several eigenmode component signals(IMFs)are obtained.Finally,the improved dense neural network model with channel attention module(MECANet)is introduced to extract and identify the deep fault features of the decomposed IMF component signals,and the fault identification of the rolling bearing is completed.The experimental results show that the accuracy of the optimized VMD combined with the improved DenseNet model for fault identification of rolling bearings has reached 99.23%.Compared with some other common fault diagnosis models,the accuracy is significantly improved,and there is a small gap between it and the advanced fault diagnosis model,which verifies the effectiveness of this model in fault diagnosis of rolling bearings.
rolling bearingvariational mode decompositionmulti-population differential evolutiondense neural networkMECANetfault diagnosis
万方数据
维普
