BEARING FAULT DIAGNOSIS METHOD BASED ON MULTI-SCALE AND MULTI-PATH ENSEMBLE NETWORK
QI BoWei
LI YuanYuan
SONG LiYuan
Abstract：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 97.5%and 98.25%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.
Keywords：BearingConvolution neural networkMulti-scale and multi-pathFault diagnosisEnsemble network
Publication Date：2024-08-15
Online Publishing Date：2026-09-12(First online date of this platform, not the publication date of the document)
Pages：9( 778-786 )