BEARING FAULT DIAGNOSIS METHOD BASED ON MULTI⁃SCALE AND MULTI⁃PATH ENSEMBLE NETWORK - QI BoWei; LI YuanYuan; SONG LiYuan (Jixie qiangdu)
To address the problems such as complex convolutional neural network parameters,deep layers and weak generalization performance,a multi⁃scale multi⁃path convolutional neural network(MCS⁃CNN)based bearing fault diagnosis method cas proposed.Firstly,a multi⁃scale convolution block was proposed to reduce the number of network parameters and increase the network width by using depthwise convolution and pointwise convolution,so as to extract multi⁃scale features effectively.Secondly,an ensemble block was proposed to increase the network depth by connecting low⁃level and high⁃level features through multiple paths,thereby improving the diagnostic accuracy of the model.Finally,the effectiveness of the method was verified on the Case Western Reserve University bearing dataset.The results show that the fault diagnosis accuracy of the proposed method can reach 975%and 9825%in high⁃noise and cross⁃load scenarios,and the accuracy in mixed scenarios is improved by more than 15%compared to existing diagnosis methods,which prove the robustness and generalisability of the proposed method.