首页|基于两种改进RedNet的滚动轴承故障诊断方法研究

基于两种改进RedNet的滚动轴承故障诊断方法研究

Research on the Rolling Bearing Fault Diagnosis Based on Two Improved RedNet

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RedNet网络自带的余弦退火算法易使学习率陷入局部极小值,出现拟合现象,导致精度过低.针对此问题,对RedNet进行改进处理,提出了两种MicroNet-RedNet和MobileNetV3-RedNet新型网络.基于RedNet的Involution核思想,用MicroNet网络的微分解卷积和Dynamic Shift-Max动态激活函数对RedNet网络进行改进处理,提出了 MicroNet-RedNet新型网络;利用MobileNetV3网络的h-swish激活函数和Squeeze-and-Excitation模块对RedNet进行改进处理,提出Mobile-NetV3-RedNet新型网络.通过对滚动轴承的实测内圈、外圈和滚动体3种故障的诊断分析可知:所提MicroNet-RedNet和所提MobileNetV3-RedNet可有效地诊断上述故障,诊断精度分别高达98.57%和93.81%,且较传统CNN和原算法RedNet的诊断精度提高很多.
The cosine annealing algorithm of RedNet network is easy to make the learning rate fall into the local minimum and the over-fitting phenomenon occurs,which leads to the low accuracy.In view of the problems,RedNet was improved and two new networks of MicroNet-RedNet and MobileNetV3-RedNet were proposed.Based on the Involution kernel idea of RedNet,the micro-factorized con-volution and Dynamic Shift-Max activation function of MicroNet were used to improve RedNet,and thus a new network MicroNet-Red-Net was proposed.The h-swish activation function and Squeeze-and-Excitation module of MobileNetV3 were applied to improve Red-Net,and thus a new network MobileNetV3-RedNet was proposed.Based on the measured inner ring fault,outer ring fault and rolling ele-ment fault of the rolling bearing,it can be seen that the above faults can be diagnosed by the two proposed networks of MicroNet-Red-Net and MobileNetV3-RedNet effectively.The accuracies are as high as 98.57%and 93.81%respectively,which are much higher than those got by traditional CNN and the original RedNet.

rolling bearingRedNetMicroNetMobileNetV3

郑直、单思然、曾魁魁、王志军、朱勇

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华北理工大学机械工程学院,河北唐山 063210

江苏大学国家水泵及系统工程技术研究中心,江苏镇江 212013

滚动轴承 RedNet网络 MicroNet网络 MobileNetV3网络

河北省自然科学基金河北省高层次人才项目唐山市科技创新团队培养计划河北省科技重大专项

E2022209086B202000303321130208D22282203Z

2024

机床与液压
中国机械工程学会 广州机械科学研究院有限公司

机床与液压

CSTPCD北大核心
影响因子:0.32
ISSN:1001-3881
年,卷(期):2024.52(4)
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