基于RQA与SVM的声发射信号检测识别方法

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振动与冲击 ›› 2016, Vol. 35 ›› Issue (2) : 97-103.

论文

司莉1,4，毕贵红2,4，魏永刚3,4，陶然4，张寿明1

作者信息 +

Detection and identification of acoustic emission signals based on recurrence quantification analysis and support vector machines

SI Li1,4，BI Gui-hong2,4，WEI Yong-gang3,4, TAO Ran4，ZHANG Shou-ming1

Author information +

文章历史 +

摘要

针对裂纹声发射信号检测问题，提出基于递归定量分析与支持向量机相结合的新型检测方法。利用小波阈值去噪原理，对采集的声发射信号进行去噪，将递归定量分析引入声发射信号检测，提取递归定量分析的量化特征参数，结合支持向量机对模拟裂纹声发射信号进行识别。并实验验证该方法的可行性。

Abstract

To prevent the leakage accidents of pipes and boiler, the key lies in whether the signals of cracks and small leaks can be detected effectively. For the problem of acoustic emission signals detection, a new method of detecting acoustic emission signals based on RQA and SVM was described. Firstly, the theory of wavelet threshold de-noising was used to reduce the noise signal. Secondly, the principle of RQA was borrowing to detection of acoustic emission signal, by calculation, we can get some quantifiable feature parameters. Using these parameters as SVM input parameters, the simulation acoustic emission signals of cracks can be identified. In the experimental conditions, the feasibility of this method has been validated.

导出引用

司莉1,4，毕贵红2,4，魏永刚3,4，陶然4，张寿明1. 基于RQA与SVM的声发射信号检测识别方法[J]. 振动与冲击, 2016, 35(2): 97-103

SI Li1,4，BI Gui-hong2,4，WEI Yong-gang3,4, TAO Ran4，ZHANG Shou-ming1. Detection and identification of acoustic emission signals based on recurrence quantification analysis and support vector machines[J]. Journal of Vibration and Shock, 2016, 35(2): 97-103

参考文献

[1] 林丽.船舶与海洋结构物设计制造[D].大连:大连理工大学,2008.
[2] 王朝晖,张来斌,辛若佳,等.声发射技术在管道泄漏检测中的应用[J].中国石油大学学报:自然科学版,2007,31(5):87- 88.
WANG Chao-hui, ZHANG Lai-bin,XIN Ruo-jia, et al. Application of acoustic emission technique in pipeline leakage detection[J]. Journal of China University of Petroleum:Edition of Natural Science,2007,31(5):87-88.
[3] 付俊涛,李玉星,孟令雅,等.输气管道泄漏音波与干扰信号特征提取[J]. 石油化工高等学校学报,2012,25(2):53-54.
FU Jun-tao, LI Yu-xing, MENG Ling-ya,et al. Characteristic extraction of acoustic leak signals and interfering signals for natural gas pipeline[J]. Journal of Petrochemical Universities, 2012, 25 (2):53-54.
[4] 沈继忱,王春雨,王慧丽. 管道泄漏诊断方法研究[J]. 化工自动化及仪表,2012,39(3):309-311.
SHEN Ji-chen, WANG Chun-yu, WANG Hui-li. Method research for pipeline leakage diagnosis[J]. Control and Instruments in Chemical Industry,2012,39(3):309-311.
[5] 林丽,赵德有.局域波神经网络海洋平台AE信号识别研究[J]. 哈尔滨工程大学学报,2008,29(7):663-667.
LIN Li. ZHAO De-you. Identification of acoustic emission signals of offshore structures with both local wave method and neural network[J]. Journal of Harbin Engineering University, 2008,29(7):663-667.
[6] 毛汉颖, 毛汉领,周洁,等.基于BP神经网络的混流式水轮机裂纹声发射源定位方法[J].无损检测,2008,30(7):426-429.
MAO Han-ying, MAO Han-ling, ZHOU Jie, et al. Acoustic emission source location for the crack of francis turbine runner based on BP neural network[J].Nondestructive
Testing, 2008,29(7):663-667.
[7] 张晓涛,唐立伟,王平,等.基于SVD与Fast Kurtogram算法的滚动轴承声发射故障诊断[J].振动与冲击,2014,33(10): 101-105.
ZHANG Xiao-tao, TANG Li-wei, WANG Ping, et al. Acoustic emission fault diagnosis of rolling bearings based SVD and Fast Kurtogram algorithm[J]. Journal of Vibration and Shock,2014,33(10):101-105.
[8] 毛汉领,王向红,黄振峰. 基于小波包分析的声发射信号界面的衰减研究[J].振动与冲击,2008,27(9):139-141.
MAO Han-ling, WANG Xiang-hong, HUANG Zhen-feng. Attenuation of acoustic emission signals due to discontinuous interface based on wavelet packet analysis[J]. Journal of Vibration and Shock,2008,27(9):139-141.
[9] 王向红,朱昌明,毛汉领, 等. 基于小波神经网络的水轮机叶片裂纹源的定位技术[J].上海交通大学学报,2008,42(8): 1301-1309.
WANG Xiang-hong, ZHU Chang-ming,MAO Han-ling, et al. Source location of cracks of turbine blades based on wavelet neural network[J]. Journal of Shanghai Jiaotong University, 2008,42(8):1301-1309.
[10] 王向红,朱昌明,毛汉领, 等. 基于核主成分分析及支持向量机的水轮机叶片裂纹源定位[J].振动与冲击,2010,29(11): 226-229.
WANG Xiang-hong, ZHU Chang-ming. MAO Han-ling, et al. Source location of cracks in a turbine blade based on kernel principal component analysis and support vector machines[J]. Journal of Vibration and Shock,2010,29(11):226-229.
[11] 王向红,胡宏伟,张志勇,等. 微弱裂纹信号的稀疏编码提取[J].振动工程学报,2013,26(3):311-317.
WANG Xiang-hong, HU Hong-wei, ZHANG Zhi-yong,et al. Extraction of weak crack signals by sparse code[J]. Journal of Vibration Engineering,2013,26(3):311-317.
[12] 刘勇.基于泄漏电流递归分析的绝缘子状态检测研究[D].天津:天津大学,2009.
[13] 闫润强.语音信号动力学特性递归分析[D].上海:上海交通大学,2006.
[14] 高尚.支持向量机及其个人信用评估[M].西安:西安电子科技大学出版社,2013.
[15] 柯慧,顾洁.电能质量信号的小波阈值去噪[J]. 电力系统及其自动化学报,2010,22(2):103-108.
KE Hui, GU Jie. Wavelet threshold de-noising of power system signals[J]. Proceedings of the Chinese Society of Universities,2010,22(2):103-108.
[16] 杨波.基于支持向量机的中学教师评价系统研究[D].长春:东北师范大学,2008.
[17] Chang C C, Lin C J. Libsvm: a library for support vector machines[R]. Taiwan: Department of Computer Science National Taiwan University,2011.