计算机工程与应用 ›› 2021, Vol. 57 ›› Issue (6): 239-246.DOI: 10.3778/j.issn.1002-8331.1912-0356

• 工程与应用 • 上一篇    下一篇

结合马田系统-SVM的滚动轴承故障模式分类研究

韩卫宇,程龙生   

  1. 南京理工大学 经济管理学院,南京 210094
  • 出版日期:2021-03-15 发布日期:2021-03-12

Research on Roling Bearing Failure Mode Classification Based on MTS and SVM

HAN Weiyu, CHENG Longsheng   

  1. School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China
  • Online:2021-03-15 Published:2021-03-12

摘要:

为了有效地确定滚动轴承的故障类型和受损程度,提出了结合马田系统和SVM的滚动轴承故障模式分类方法。利用EEMD方法对原始振动信号进行分解,得到一系列IMF。经过故障敏感IMF选取方法筛选IMF后计算其时域和频域特征参数以及原始信号的能量熵参数,构造初始的多维特征空间。运用马田系统中的正交表和信噪比进行特征降维,得到精简特征空间。接下来使用偏二叉树方法构建支持向量机多分类模型。通过实验数据进行模型验证,结果表明该方法可以实现滚动轴承故障模式分类。

关键词: 故障模式分类, 马田系统(MTS), 支持向量机(SVM), 集合经验模态分解(EEMD)

Abstract:

In order to determine the type and degree of damage of rolling bearing faults effectively, a classification method of rolling bearing fault modes combining MTS and SVM is proposed. The original vibration signal is decomposed using the EEMD method to obtain a series of IMF. After selecting the IMF through the fault-sensitive IMF selection method, the time and frequency domain characteristic parameters and the energy entropy parameters of the original signal are calculated to construct the initial multi-dimensional feature space. The orthogonal table and the signal-to-noise ratio in the MTS are used to reduce the feature dimensions to obtain a reduced feature space. The partial binary tree method is used to build a SVM multi-classification model. The model is verified by experimental data, and the results show that the method can classify rolling bearing failure modes.

Key words: failure modes classification, Mahalanobis-Taguchi System(MTS), Support Vector Machine(SVM), Ensemble Empirical Mode Decomposition(EEMD)