首页|基于EEMD和CNN-SVM的滚动轴承故障诊断

基于EEMD和CNN-SVM的滚动轴承故障诊断

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针对滚动轴承振动信号易受外界噪声干扰、传统的故障诊断方法难以提取故障特征以及准确率低等问题，提出一种基于集合经验模态分解(EEMD)和卷积神经网络结合支持向量机(CNN-SVM)的滚动轴承故障诊断方法。利用EE-MD算法对原始振动信号进行分解得到本征模态函数(IMF)分量，再由相关系数筛选最佳分量进行信号重构，得到降噪后的振动信号。将重构降噪后的振动信号转换为二维特征图输入卷积神经网络进行训练提取特征。最后将提取到的稀疏代表特征向量输入到支持向量机进行故障分类。实验结果表明:所提方法能有效降低噪声干扰，便于提取故障特征，与传统的故障诊断方法相比准确率更高，诊断速度更快。
Rolling Bearing Fault Diagnosis Based on EEMD and CNN-SVM
Aiming at the problems that rolling bearing vibration signals are susceptible to external noise interference,traditional fault diagnosis methods are difficult to extract fault features and have low accuracy,a rolling bearing fault diagnosis method based on en-semble empirical modal decomposition(EEMD)and convolutional neural network combined with support vector machine(CNN-SVM)was proposed.The EEMD algorithm was used to decompose the original vibration signal to obtain the intrinsic modal function(IMF)components,and then the correlation coefficient was used to filter the best components for signal reconstruction,and the noise-reduced vibration signal was obtained.The reconstructed and noise-reduced vibration signal was converted into a 2D feature map and inputted into the convolutional neural network to train and extract features.Finally,the extracted sparsely represented feature vector was inputted into support vector machine for fault classification.The experimental results show that the proposed method can effectively reduce the noise interference and easily extract fault features.Compared with traditional fault diagnosis methods,the accuracy is higher,and the di-agnosis speed of the improved model is faster.

rolling bearingensemble empirical modal decompositionconvolutional neural networkfault diagnosis

朱俊杰、张清华、朱冠华、苏乃权

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广东石油化工学院自动化学院,广东茂名 525000

吉林化工学院信息与控制工程学院,吉林吉林 132022

滚动轴承 集合经验模态分解 卷积神经网络 故障诊断

国家自然科学基金重点项目广东省自然科学基金面上项目茂名市2023年省科技创新战略专项茂名市2023年省科技创新战略专项茂名绿色化工研究院扬帆计划广东石油化工学院博士启动项目

619330132022A15150105992023S0010012023S001011MMGCIRI-2022YFJH-Y-0092020bs006

2024

10.3969/j.issn.1001-3881.2024.17.032

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(17)