摘要
为了有效监测复杂工况下分布式驱动电动汽车用轮毂电机的运行状态,提高其轴承故障的识别准确率,提出一种基于多信息加权融合和二维卷积神经网络(MIWF-2DCNN)的故障诊断方法。首先,将轮毂电机轴承的多方位振动监测信号分别进行二维数据重构和时频变换,逐一转化成灰度图后按照方位顺序堆叠成时域灰度图集和时频域灰度图集,作为故障诊断模型的输入;其次,将高效通道注意力机制(ECANet)的网络结构进行改进,提出了改进高效通道注意力机制(iECANet),其核心思想是在全局平均池化(GAP)基础上添加上全局最大池化(GMP)分支,基于有效信息的贡献度更新各分支的权重系数,进而提取时域和时频域的故障特征,实现了多信息加权融合;再次,利用GMP简化传统二维卷积神经网络(2DCNN)模型的一层全连接层,实现了网络轻量化。最后,基于轮毂电机不同工况下实验数据,进行同一工况下对应验证、不同工况下交叉验证及消融实验验证。结果表明所提的MIWF-2DCNN模型能够有效提取轮毂电机轴承故障特征,在复杂环境和多变工况下故障识别率保持在95%以上,整体优于传统的LeNet-5、1DCNN模型。
A fault diagnosis method based on multi-information weighted fusion and two-dimensional convolutional neural network(MIWF-2DCNN)is proposed to effectively monitor the operating status of hub motors for distributed electric vehicles under complex operating conditions and improve the accuracy of bearing fault identification.Firstly,the multi-directional vibration monitoring signals of in-wheel motor bearing were reconstructed by two-dimensional data reconstruction and time-frequency transformation respectively,and then converted into grayscale images one by one.According to the direction order,the time-domain grayscale atlas and time-frequency domain grayscale atlas were stacked as the input of the fault diagnosis model.Secondly,the network structure of efficient channel attention mechanism(ECANet)was improved,and the improved efficient channel attention mechanism(iECANet)was proposed.The core idea of IECANET was to add a global maximum pooling(GMP)branch on the basis of global average pooling(GAP),and update the weight coefficient of each branch based on the contribution of effective information.Then,the fault features in time domain and time�frequency domain were extracted to realize the weighted fusion of multi-information.Thirdly,GMP was used to simplify a fully connected layer of the traditional two-dimensional convolutional neural network(2DCNN)model to achieve network lightweight.Finally,based on the experimental data of in-wheel motor under different working conditions,the corresponding verification under the same working condition,cross validation under different working conditions and ablation experimental verification were carried out.The results show that the proposed MIWF-2DCNN model can effectively extract the fault features of in-wheel motor bearing,and the fault recognition rate remains above 95%in complex environments and variable working conditions,which is better than the traditional LeNet-5 and 1DCNN models.
作者
戈淳
宋子为
商嘉桐
薛红涛
王天鸶
Ge Chun;Song Ziwei;Shang Jiatong;Xue Hongtao;Wang Tiansi(School of Automotive and Traffic Engineering,Jiangsu University,Zhengjiang 212013,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2024年第9期127-135,共9页
Journal of Electronic Measurement and Instrumentation
基金
国家自然科学基金(52272367)项目资助。
关键词
轮毂电机
二维卷积神经网络
多信息加权融合
故障诊断
通道注意力
in-wheel motor
two-dimensional convolutional neural network
multi information weighted fusion
fault diagnosis
channel attention
