基于多特征提取和改进马田系统的滚动轴承故障分类方法研究  

Fault classification method for rolling bearings based on the multi-featureextraction and modified Mahalanobis-Taguchi system

作　　者：彭宅铭 程龙生[1] 詹君 姚启峰 PENG Zhaiming;CHENG Longsheng;ZHAN Jun;YAO Qifeng(School of Economics and Management,Nanjing University of Science and Technology,Nanjing 210094,China)

机构地区：[1]南京理工大学经济管理学院,南京210094

出　　处：《振动与冲击》2020年第6期249-256,共8页Journal of Vibration and Shock

基　　金：江苏省研究生科研与实践创新计划项目(KYCX18_0487)。

摘　　要：为提高旋转机械的使用效率,及时识别滚动轴承的潜在故障,提出一种基于多特征提取和改进马田系统(MTS)的故障分类方法。通过时域、频域和自适应白噪声的完备经验模态分解(CEEMDAN)提取多维特征,构建初始特征集。结合马田系统和有向非循环图(DAG)的特点,构建DAG-MTS多分类模型,并将其运用到轴承故障诊断中。利用滚动轴承故障数据测试该模型的有效性和实用性,结果表明,该模型能够准确识别出滚动轴承的故障。In order to improve the running efficiency of rotating machinery and to identify its potential failure,a fault classification method based on the multi-feature extraction and improved Mahalanobis Taguchi system(MTS)was proposed.The methods of time domain,frequency domain and the complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)were combinedly used to obtain a multi-dimensional feature set.Integrating the advantages of the MTS and the directed acyclic graph(DAG),a DAG-MTS multi-class classification model was constructed and applied to bearing fault diagnosis.The effectiveness and applicability of the model was verified by using experimental data.The results show that the model can quickly and accurately identify the fault of rolling bearings.

关 键 词：滚动轴承 自适应白噪声的完备经验模态分解(CEEMDAN) 马田系统(MTS) 有向非循环图(DAG) 故障诊断 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]