一种基于EMDFICA-CNN的滚动轴承故障诊断方法  

A Fault Diagnosis Method of Rolling Bearings Based on EMDFICA-CNN

作　　者：王文青 李光鑫 陈勇[1] 张建军[2] 刘睿[1] WANG Wenqing;LI Guangxin;CHEN Yong;ZHANG Jianjun;LIU Rui(Tianjin Key Laboratory of Power Transmission and Safety Technology for New Energy Vehicles,Hebei University of Technology,Tianjin 300130,China;School of Mechanical Engineering,Hebei University of Technology,Tianjin 300130,China)

机构地区：[1]河北工业大学天津市新能源汽车动力传动与安全技术重点实验室,天津300130 [2]河北工业大学机械工程学院,天津300130

出　　处：《噪声与振动控制》2021年第4期94-100,共7页Noise and Vibration Control

基　　金：国家重点研发计划资助项目(2018YFE0207000);河北省全职引进高端人才资助项目(20181228)。

摘　　要：针对滚动轴承故障信号信噪比低、特征学习效率低、诊断模型结构复杂等问题,提出一种基于经验模态分解快速独立成分分析(Empirical mode decomposition fast independent component analysis,EMDFICA)与卷积神经网络(Convolution neural network,CNN)相结合的滚动轴承故障诊断方法。首先,通过经验模态分解预处理得到原始振动信号的内禀模式函数(Intrinsic mode function,IMF);然后,对提取的IMF分量进行快速独立成分分析(Fast independent component analysis,FICA)并获得特征ICA分量;最后,将得到的ICA分量基于CNN模型进行训练、测试,实现滚动轴承智能诊断分类识别。实验结果表明:利用EMDFICA对振动信号进行预处理,能够准确高效提取故障特征。通过与其他模型的诊断结果对比发现,所提出CNN模型能实现高精度故障诊断。Aiming at the problems of low signal-to-noise ratio of rolling element bearing fault signals,low efficiency of feature learning and complex structure of diagnosis models,a rolling element bearing fault diagnosis method based on empirical mode decomposition fast independent component analysis(EMDFICA)and convolutional neural network(CNN)is proposed.Firstly,the intrinsic mode functions(IMF)of the original vibration signals are obtained through empirical mode decomposition(EMD)preprocessing method.Then,the extracted IMF components are analyzed by fast independent component analysis method and the characteristic ICA components are obtained.Finally,the ICA components are trained and tested by CNN model,and the intelligent diagnosis and classifications of rolling bearing are realized.The experimental results show that the EMDFICA method can extract the fault features accurately and effectively in the pre-processing of vibration signals.Compared with the diagnosis results of other models,the proposed CNN model can realize high precision fault diagnosis.

关 键 词：故障诊断 独立成分分析 经验模态分解 卷积神经网络 滚动轴承 

分 类 号：TH165.3[机械工程—机械制造及自动化] TN911.7[电子电信—通信与信息系统]