| 王一鹏,陈学振,李连玉.基于小波包混合特征和支持向量机的机床主轴轴承故障诊断研究[J].电子测量与仪器学报,2021,35(2):59-64 |
| 基于小波包混合特征和支持向量机的机床主轴轴承故障诊断研究 |
| Research on fault diagnosis of machine spindle bearing based on wavelet packet mixing feature and SVM |
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| DOI： |
| 中文关键词: 数控机床 小波包 t-分布式随机邻域嵌入 支持向量机 |
| 英文关键词:CNC wavelet packet t SNE SVM |
| 基金项目:国家科技重大专项（2017ZX04002001）资助 |
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| 摘要点击次数: 853 |
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| 中文摘要: |
| 主轴轴承作为机床关键零部件，针对轴承故障信息比较复杂难以获取，并且故障数据样本少问题，提出了基于小波包混合特征和支持向量机（SVM）的数控机床轴承故障诊断方法。首先对轴承振动信号进行小波包分解和重构，提取信号的混合特征构建联合特征空间；然后使用t-分布式随机邻域嵌入法对样本数据进行降维，观测混合特征样本集的数据分布；最后使用非线性SVM进行故障分类。经过现场数控机床数据验证，对主轴轴承内圈、外圈和滚珠的故障识别的准确率为100%，与线性SVM以及BP神经网络的故障分类效果来比较,该方法能更加精准地识别出了数控机床主轴轴承故障。 |
| 英文摘要: |
| Based on the research background of the CNC spindle bearing, this paper proposes a fault diagnosis method combining wavelet packet mixing feature and support vector machine（SVM）, aiming at the problem that bearing fault information is complex and difficult to obtain and fault data samples are few. First, carry out wavelet packet decomposition and reconstruction of the bearing vibration signal, and extract the mixed features of the signal to construct a joint feature space. Then use t distributed stochastic neighbor embedding (t SNE) method to observe the distribution of sample data and observe the data distribution of the mixed feature sample set. Finally, a nonlinear SVM is used for fault classification. The Experimental results show that the accuracy is 100% for the fault identification of the spindle bearing inner ring, outer ring and ball. Compared with the fault classification effect of linear SVM and BP neural network, this method has achieved good results in the application of fault diagnosis of spindle bearing of CNC. |
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