RESEARCH ON ROLLING BEARING FAULT DIAGNOSIS BASED ON MA OPTIMIZATION OF CNN - TIAN LiYong; ZHAO JianJun; YU Ning (Jixie qiangdu)
Aiming at the high dependence of super parameter selection on artificial experience in rolling bearing state identification based on convolution neural network(CNN),a fault diagnosis model(CNN⁃MA)based on mayfly algorithm(MA)was proposed.Firstly,the model used the powerful optimization ability of MA,took the diagnostic accuracy of CNN as the optimization objective,and adaptively adjusted the super parameters in CNN.Secondly,the normalized original signal image set was used to preserve the characteristics of the signal as much as possible.Finally,in order to evaluate the effectiveness of the parameters in the optimization model,compared with the CNN model optimized by particle swarm optimization(PSO)algorithm.The results show that the proposed model has more stable performance,higher recognition accuracy and good anti⁃noise ability.It fully shows the feasibility and reliability of CNN⁃MA model in fault diagnosis of rolling bearings.