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基于EEMD和小波包变换的早期故障敏感特征获取

Keywords: 早期故障,特征获取,总体平均经验模态分解,小波包

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Abstract:

提出一种基于总体平均经验模态分解和小波包变换的方法,进行早期故障敏感特征的获取,构建早期故障诊断模型.该方法首先应用EEMD对现场采集的振动信号进行分解,分离出不同频率成分的特征信号,选择与原信号相关系数最大的IMF分量进行信息重构;面向重构的IMF分量采用WPT进行分解,得到各个节点的小波系数;最后使用Hilbert变换提取小波包系数的包络,计算功率谱,准确获得早期故障的敏感特征.通过对仿真信号的分析验证了该方法对故障诊断的有效性.将该方法应用于实测的滚动轴承的内圈、外圈和滚动体故障诊断,诊断结果均表明该方法可有效提取早期故障敏感特征,故障诊断快速准确.

References

[1]  高宏力,刘庆杰,黄柏权,等.数控机床故障预测与健康管理系统关键技术[J].计算机集成制造系统,2010,16(10):2217-2226. Gao Hongli, Liu Qingjie, Huang Baiquan, et al. Key techniques of fault prediction and health management system in NC machine tool[J]. Computer Integrated Manufacturing Systems, 2010, 16(10):2217-2226. (in Chinese)
[2]  王晓冬,何正嘉,訾艳阳.多小波自适应构造方法及滚动轴承复合故障诊断研究[J].振动工程学报,2010,23(4):438-444. Wang Xiaodong, He Zhengjia, Zi Yanyang. Adaptive construction of multiwavelet and research oncomposite fault diagnosis of rolling bearing[J]. Journal of Vibration Engineering, 2010, 23(4):438-444. (in Chinese)
[3]  王红军,张建民,徐小力.基于支持向量机的机械系统状态组合预测模型研究[J].振动工程学报,2006,19(2):242-245. Wang Hongjun, Zhang Jianmin,Xu Xiao li. Study on combination trend prediction technology for mechaninery system based on SVM[J]. Journal of Vibration Engineering, 2006,19(2):242-245. (in Chinese)
[4]  Wang Hongjun, Wang Hongfeng. Study of flue gas turbine fault diagnosis technology based on EMD and VPRS[C]//Proceedings of International Conference on Reliability Maintainability and Safety. Chengdu, China: IEEE, 2009:813-815.
[5]  张超,陈建军,郭迅,等.基于EMMD分解的滚动轴承故障诊断[J].机械强度,2012(5):650-656. Zhang Chao, Cheng Jianjun, Guo Xun, et al. Composite fault diagnosis for rolling bearing of electric machine based on emmd[J]. Journal of Mechanical Strength, 2012(5):650-656. (in Chinese)
[6]  吕建新,吴虎胜,田杰.EEMD的非平稳信号降噪及其故障诊断应用[J].计算机工程与应用,2011,47(28):223-227. Lü Jianxin, Wu Husheng, Tian Jie. Signal denoising based on EEMD for non-stationary signals and its application in fault diagnosis[J]. Computer Engineering and Applications, 2011,47(28):223-227. (in Chinese)
[7]  董文智,张超.基于EEMD能量熵和支持向量机的轴承故障诊断[J].机械设计与研究,2011(5):53-57. Dong Wenzhi, Zhang Chao. A bearing fault diagnosis method based on EEMD energy entropy and SVM[J]. Machine Design & Research, 2011(5):53-57. (in Chinese)
[8]  Ypma A. Learning methods for machine vibration analysis and health monitoring[D]. Delft: Delft University of Technology, 2001.

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