基于柔性残差神经网络的滚动轴承智能故障诊断方法  

Intelligent fault diagnosis method for rolling bearings based on flexible residual neural network

作　　者：陈闯 李先锋 史建涛 岳冬冬 CHEN Chuang;LI Xianfeng;SHI Jiantao;YUE Dongdong(College of Electrical Engineering and Control Science,Nanjing Tech University,Nanjing 211816,China;School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)

机构地区：[1]南京工业大学电气工程与控制科学学院,南京211816 [2]上海交通大学电子信息与电气工程学院,上海200240

出　　处：《工程科学学报》2025年第3期480-488,共9页Chinese Journal of Engineering

基　　金：国家自然科学基金资助项目(62303217,62373184,62333010);中国博士后科学基金资助项目(2024M751977);江苏省自然科学基金资助项目(BK20211502);江苏省高等学校自然科学基金资助项目(23KJB510006)。

摘　　要：滚动轴承作为旋转机械的重要组成部分,其正常运行直接影响机器的使用寿命和运行状态.为了提高滚动轴承故障诊断的准确性,本文提出一种基于动态减法平均优化器(DSABO)和平行注意力模块(PAM)的柔性残差神经网络(FResNet),用于滚动轴承故障诊断.具体而言,首先设计一种基于卷积神经网络的柔性残差模块来构建FResNet.该模块允许在DSABO迭代时更改卷积层数、卷积核数和跳跃连接数,从而增强网络故障特征提取能力并减少网络退化.其次,设计具有卷积层的PAM来融合通道注意力和空间注意力输出权重,通过与滚动轴承运行数据结合,实现数据特征增强.于是,DSABO、PAM和FResNet的集成形成了一个有效的滚动轴承故障诊断模型,命名为DSABO-PAM-FResNet.最后,利用美国凯斯西储大学滚动轴承故障数据集验证所提DSABO-PAM-FResNet模型的可行性和有效性.结果显示,在信噪比为–6 dB环境下所提模型对滚动轴承故障诊断的准确率为97.18%,证明所提模型具有较好的抗噪能力;在0.75 kW、1.5 kW和2.25 kW不同负载条件下,所提模型对滚动轴承故障诊断的平均准确率为98.2%,证明所提模型具有良好的变工况诊断适应能力.与其他智能故障诊断方法的对比结果表明,所提DSABO-PAM-FResNet模型的诊断精度更高,为滚动轴承故障诊断提供了一种新的有效智能方法.Rolling bearings play a crucial role in rotating machinery,and their efficient operation is vital for the machine’s longevity and performance.In numerous real-world situations,diagnosing faults in rolling bearings presents significant challenges.Signals obtained from industrial applications often contain unavoidable noise,complicating analysis.Additionally,the intricate working conditions in actual operations can greatly influence bearing signal characteristics.Consequently,traditional diagnostic techniques struggle to effectively handle the effects of varying loads and noise.To improve the accuracy of fault diagnosis for rolling bearings in noisy and variable working conditions,a new approach using a flexible residual neural network(FResNet)is introduced.This network is built on a dynamic subtraction average-based optimizer(DSABO)and a parallel attention module(PAM).The core of FResNet is a flexible residual module based on convolutional neural networks,which allows for adjustments in the number of convolutional layers,convolutional kernels,and skip connections during optimization.These design features improve the network’s ability to extract fault features and prevent degradation.Second,a DSABO with a dynamic position update strategy is proposed for parameter optimization of the above FResNet with the flexible residual module.This optimizer helps the model avoid being trapped in local optima,strengthening the fault diagnosis performance of the network.Third,a PAM is integrated,featuring convolutional layers that combine channel and spatial attention.This integration enhances data feature extraction by aligning it with rolling bearing operation data.Together,DSABO,PAM,and FResNet create an effective rolling bearing fault diagnosis model known as DSABO–PAM–FResNet.Finally,the feasibility and effectiveness of the proposed DSABO–PAM–FResNet model are validated using the rolling bearing fault dataset from Case Western Reserve University in the United States.The ablation experiments reveal that the DSABO

关 键 词：轴承故障诊断 柔性残差神经网络 动态减法平均优化器 平行注意力模块 噪声干扰 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]