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.