Rolling bearing fault diagnosis based on optimized A-BiLSTM
In order to improve the efficiency of hyperparameter setting and its adaptability to the model,and break the high cost and low efficiency of manual parameter setting,a fault diagnosis method for rolling bearings based on the honey badger algorithm(HBA)optimizing bi-directional long short-term memory(BiLSTM)with attention mechanism(HBA-A-BiLSTM)is proposed.Firstly,search the optimal hyperparameter combination of the A-BiLSTM model through HBA.Secondly,the fault diagnosis performance is tested based on the A-BiLSTM model under the optimal hyperparameters.Finally,the generalization ability of the model is tested based on the datasets under different working conditions.The CWRU dataset is used to verify the fault diagnosis effect of the proposed method,which is used the diagnostic accuracy and the confusion matrix to evaluate.It is shown that,compared with other swarm intelligence optimization algorithms,the HBA has better global searching performance and faster convergence speed.The fault diagnosis accuracy of the optimized model has reached 99.5％,which has a good effect,also under different working conditions,it can achieve stable and accurate fault diagnosis performance,and has strong generalization ability.
fault diagnosishoney badger algorithmparameters optimizationbidirectional long short-term memory networkattention mechanism
万方数据
维普
