[1]邓艾东,赵力,包永强,等.噪声环境下基于小波熵的声发射识别[J].东南大学学报(自然科学版),2009,39(6):1151-1155.[doi:10.3969/j.issn.1001-0505.2009.06.013]
 Deng Aidong,Zhao Li,Bao Yongqiang,et al.Recognition of acoustic emission based on wavelet entropy in noisy condition[J].Journal of Southeast University (Natural Science Edition),2009,39(6):1151-1155.[doi:10.3969/j.issn.1001-0505.2009.06.013]
点击复制

噪声环境下基于小波熵的声发射识别()
分享到:

《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
39
期数:
2009年第6期
页码:
1151-1155
栏目:
机械工程
出版日期:
2009-11-20

文章信息/Info

Title:
Recognition of acoustic emission based on wavelet entropy in noisy condition
作者:
邓艾东1 赵力2 包永强3 高亹1
1 东南大学火电机组振动国家工程研究中心,南京 210096; 2 东南大学信息科学与工程学院,南京 210096; 3 南京工程学院通信工程学院,南京 210067
Author(s):
Deng Aidong1 Zhao Li2 Bao Yongqiang3 Gao Wei1
1 National Engineering Research Center of Turbo-generator Vibration,Southeast University,Nanjing 210096, China
2 School of Information Science and Engineering,Southeast University,Nanjing 210096,China
3 School of Communication Engineering,Nanjing Institute of Technology,Nanjing 210067,China
关键词:
声发射 小波熵 识别
Keywords:
acoustic emission wavelet entropy recognition
分类号:
TH165.3
DOI:
10.3969/j.issn.1001-0505.2009.06.013
摘要:
针对声发射技术在旋转机械故障检测中的强噪声干扰问题,提出了一种基于小波熵的声发射检测算法.该算法首先给定一个合理的阈值,对声发射信号进行小波分解.然后进行分帧处理,使信号在较短的时间间隔内保持特性基本不变,从而求出每一帧信号的小波熵.通过比较每一帧信号的小波熵值与阈值的大小,判断该信号为声发射帧还是噪声帧.为了检验算法的检测效果,在转子实验台上获得碰摩声发射信号,并在测试数据上叠加不同信噪比的高斯白噪声和非平稳噪声,进行声发射识别.实验结果表明:该算法具有较高的识别正确率; 在低信噪比环境下,通过调整阈值的可调参数可以有效提高识别的正确率.
Abstract:
In acoustic emission(AE)technique, to avoid the serious noise disturbance in the fault diagnosis of rotary machine, a recognition algorithm based on wavelet entropy(WE)is proposed. First, through setting up an appropriate threshold, an AE signal is decomposed by wavelet transform. Secondly, the AE signal is divided into some equal frames to keep the characteristic approximately constant in a short time interval, and the WE of each frame can be calculated. Thirdly, through comparing the value of each WE with the threshold, it can be determined whether the frame belongs to AE frames or noise frames. To test the recognition efficiency of this algorithm, a rub impact AE signal obtained from a rotating test stand is added with white noise and non-stationary noise at various signal-to-noise ratios(SNRs), followed by AE recognition. The experimental results indicate that this algorithm has a high recognition efficiency. In a low SNR environment, the recognition efficiency can be improved by adjusting the parameter of the threshold.

参考文献/References:

[1] Kuo C C.Artificial recognition system for defective types of transformers by acoustic emission[J].Expert Systems with Applications,2009,36(7):10304-10311.
[2] Rippengill S,Worden K,Holford K M,et al.Automatic classification of acoustic emission patterns[J].Strain,2003,39(1):31-41.
[3] 易若翔,刘时风,耿荣生,等.人工神经网络在声发射检测中的应用[J].无损检测,2002,24(11):488-491.
  Yi Ruoxiang,Liu Shifeng,Geng Rongsheng,et al.Application of artificial neural network to acoustic emission testing[J].Nondestructive Testing,2002,24(11):488-491.(in Chinese)
[4] 刘国华,黄平捷,龚翔,等.基于分形维和独立分量分析的声发射特征提取[J].华南理工大学学报:自然科学版,2008,36(1):76-80.
  Liu Guohua,Huang Pingjie,Gong Xiang,et al.Feature extraction of acoustic emission signals based on fractal dimension and independent component analysis[J].Journal of South China University of Technology:Natural Science Edition,2008,36(1):76-80.(in Chinese)
[5] 金文,陈长征,金志浩,等.声发射源识别中的三比值特征提取方法研究[J].仪器仪表学报,2008,29(3):530-534.
  Jin Wen,Chen Changzheng,Jin Zhihao,et al.Study on three parameter ratio method in AE source recognition[J].Chinese Journal of Scientific Instrument,2008,29(3):530-534.(in Chinese)
[6] Van D G,Wevers M,Van Hulle M M.Wavelet packet decomposition for the identification of corrosion type from acoustic emission signals[J].International Journal of Wavelets,Multiresolution and Information Processing,2009,7(4),513-534.
[7] Kacimi S,Laurens S.The correlation dimension:a robust chaotic feature for classifying acoustic emission signals generated in construction materials[J]. Journal of Applied Physics,2009,106(2):024909.
[8] Surgeon M,Wevers M.Modal analysis of acoustic emission signals from CFRP Laminates[J].NDT and International,1999,32(6):311-322.
[9] Hyunje J,Young S.Fracture source location in thin plates using the wavelet transform of disperse waves[J].IEEE Transactions on Transonic,Ferroelectrics,and Frequency Control,2000,47(3):612-619.
[10] Mallat S.A wavelet tour of signal processing [M].2nd ed.San Diego,CA,USA:Academic Press,1999:58-67.
[11] Hasan A A,Joseph S P,Wendy C Z,et al.Wavelet entropy for subband segmentation of EEG during injury and recovery[J].Annals of Biomedical Engineering,2003,31(6):653-658.
[12] 印欣运,何永勇,彭志科,等.小波熵及其在状态趋势分析中的应用[J].振动工程学报,2004,17(2):165-167.
  Yin Xinyun,He Yongyong,Peng Zhike,et al.Study on wavelet entropy and its applications in trend analysis[J].Journal of Vibration Engineering,2004,17(2):165-167.(in Chinese)
[13] Al-Nashash H A,Thakor N V.Monitoring of global cerebral ischemia using wavelet entropy rate of change[J].IEEE Trans Biomed Eng,2005,52(12):2119-2122.
[14] Korol A M,Rasia R J,Rosso O A.Alterations of thalassemic erythrocytes detected by wavelet entropy [J].Physica A,2007,375(1):257-264.

相似文献/References:

[1]邓扬,丁幼亮,李爱群.基于声发射速率过程理论的钢绞线损伤演化试验研究[J].东南大学学报(自然科学版),2010,40(6):1238.[doi:10.3969/j.issn.1001-0505.2010.06.021]
 Deng Yang,Ding Youliang,Li Aiqun.Experimental study on damage evolution of steel strands based on acoustic emission signals and rate process theory[J].Journal of Southeast University (Natural Science Edition),2010,40(6):1238.[doi:10.3969/j.issn.1001-0505.2010.06.021]
[2]贺婷婷,钟文琪,李蔚玲,等.三相流化床流动结构特征的小波分析[J].东南大学学报(自然科学版),2014,44(3):573.[doi:10.3969/j.issn.1001-0505.2014.03.022]
 He Tingting,Zhong Wenqi,Li Weiling,et al.Characteristics of flow patterns in three-phase fluidized bed by wavelet analysis[J].Journal of Southeast University (Natural Science Edition),2014,44(6):573.[doi:10.3969/j.issn.1001-0505.2014.03.022]
[3]熊文,万毅宏,侯训田,等.声发射信号预测山体滑坡基础性试验研究[J].东南大学学报(自然科学版),2016,46(1):184.[doi:10.3969/j.issn.1001-0505.2016.01.030]
 Xiong Wen,Wan Yihong,Hou Xuntian,et al.Fundamental experiments on landslide prediction based on acoustic emission monitoring[J].Journal of Southeast University (Natural Science Edition),2016,46(6):184.[doi:10.3969/j.issn.1001-0505.2016.01.030]

备注/Memo

备注/Memo:
作者简介: 邓艾东(1968—),男,博士,副教授,addseu@163.com.
基金项目: 国家高技术研究发展计划(863计划)资助项目(2007AA04Z4334)、国家自然科学基金资助项目(60872073)、东南大学科学基金资助项目(KJ2009348).
引文格式: 邓艾东,赵力,包永强,等.噪声环境下基于小波熵的声发射识别[J].东南大学学报:自然科学版,2009,39(6):1151-1155. [doi:10.3969/j.issn.1001-0505.2009.06.013]
更新日期/Last Update: 2009-11-20