Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (6): 239-246.DOI: 10.3778/j.issn.1002-8331.1912-0356

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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



  1. 南京理工大学 经济管理学院,南京 210094


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)



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)