Acoustic Emission Diagnosis of Low-Speed Bearing Faults Based on CEEMD Energy Entropy and SVM
-
摘要: 针对低速轴承故障诊断难的问题，将互补总体平均经验模态分解（CEEMD）能量熵与支持向量机相结合对低速轴承故障进行了声发射诊断。采集不同缺陷状态的轴承声发射信号进行CEEMD分解，得到自适应的本征模态分量（IMF）；结合IMF分量的方差贡献率和互相关系数对虚假分量进行剔除，筛选出有效IMF分量。对提取的有效IMF分量计算能量熵，作为不同故障轴承的特征向量。将该特征向量输入到支持向量机（SVM），对不同故障的低速轴承进行分类识别。试验结果表明，通过方差贡献率和互相关系数能够筛选出含主要故障信息的IMF分量，同时验证了SVM相比BP神经网络对低速轴承不同故障类型的识别效果更好。Abstract: Aiming at the problem of fault diagnosis of low-speed bearing, an acoustic emission diagnosis method based on the combination of complementary ensemble empirical mode decomposition (CEEMD) energy entropy and support vector machine (SVM) is proposed. Firstly, the acoustic emission signals of bearing with different damage states are decomposed by CEEMD, thus an adaptive intrinsic mode component (IMF) is obtained. Afterwards, the combination of the variance contribution rate and IMF component mutual correlation coefficient is used to remove the false component and to sift out effective component for signal reconstruction. Due to the different energy distributions of different damage bearing, the damage state of the bearing can be characterized by the change of energy entropy. The energy entropy of the extracted effective IMF components is calculated as the feature vector of different fault bearing. The feature vector is input to the support vector machine to classify and identify the different faults. The experimental results show that the correlation coefficient and variance contribution rate can be selected with the main fault information of the IMF component. At the same time, it is proven that SVM is better than BP neural network in identifying different fault types of low speed bearings.
-
Keywords:
- acoustic emission /
- low-speed bearing /
- CEEMD /
- energy entropy /
- SVM /
- fault diagnosis
-
-
[1] 钟秉林,黄仁.机械故障诊断学[M]. 北京:机械工业出版社,2007. [2] RAY A G. Monitoring rolling contact bearings under adverse conditions[C]//IMechE Conference on Bibrations in Rotating Machinery.[S.l.]:[s.n], 1980, 187-194.
[3] Al-GHAMD A M, MBA D A.Comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size[J]. Mechanical Systems and Signal Processing, 2006, 20(7):1537-1571.
[4] MBA D, RAJ B K N, RAO. Development of acoustic emission technology for condition monitoring and diagnosis of rotating machines: bearings, pumps, gearboxes, engines and rotating structures[J]. The Shock and Vibration Digest, 2006, 38(1):3-16.
[5] 耿容生,沈功田,刘时风. 声发射信号处理和分析技术[J]. 无损检测,2002,24(1):23-28. [6] 郝如江,卢文秀,褚福磊. 声发射检测技术用于滚动轴承故障诊断的研究综述[J]. 振动与冲击, 2008, 27(3):75-79. [7] 赵一帆,齐明侠,赵继红,等.基于声发射技术的滚动轴承故障检测[J]. 轴承,2010(4):50-53. [8] 孙永生,李猛,刘恒,等.基于声发射检测技术的滚动轴承缺陷检测[J]. 无损检测,2015,37(8):17-20. [9] LEI Yaguo, HE Zhengjia, ZI Yanyang. Application of an intelligent classification method to mechanical fault diagnosis[J]. Expert Systems with Applications, 2009,36(6):9941-9948.
[10] SMITH S J. The local mean decomposition and its application to EEG perception data[J]. Journal of the Royal Society Interface,2005,2(5):443-454.
[11] WU Z H, HUANG N E. Ensemble empirical mode decomposition: a noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009, 1(1):1-41.[JP]
[12] 陈隽,李想. 运用总体经验模式分解的疲劳信号降噪方法[J].振动、测试与诊断,2011,31(1):15-19. [13] YEH J R,HUANG N E. Complementary ensemble empirical mode decomposition: a novel noise enhanced data analysis method[J]. Advances in Adaptive Data Analysis, 2010, 2(2):135-156.
[14] CORTES C, VAPNIK V. Support-vector networks[J]. Machine Learning, 1995, 20(3):273-297.
[15] CHANG C C, LIN C J, LIBSVM. A library for support vector machines[J]. ACM Transactions on Intelligent Systems and Technology,2011,2(3):1-27.
[16] 奉国和. SVM分类核函数及参数选择比较[J]. 计算机工程与应用,2011,47(3):123-125.
计量
- 文章访问数: 0
- HTML全文浏览量: 0
- PDF下载量: 0
下载:
