石油化工高等学校学报

石油化工高等学校学报 ›› 2015, Vol. 28 ›› Issue (3): 80-85.DOI: 10.3969/j.issn.1006-396X.2015.03.017

• 油气储运 • 上一篇    下一篇

基于小波变换与R B F网络的声发射信号识别

周 俊1,  王 强1,  伊 鸣2,  王 帅1   

  1.             
    ( 1. 后勤工程学院,重庆4 0 1 3 3 1; 2. 中国人民解放军6 5 1 3 3部队, 辽宁沈阳1 1 0 0 4 3)
  • 收稿日期:2014-04-01 修回日期:2014-09-26 出版日期:2015-06-25 发布日期:2015-06-23
  • 作者简介:周俊( 1 9 8 1 - ) , 男, 博士, 高级工程师, 从事信号特征提取、 信号模式识别方面研究; E - m a i l : h g z h o u 2 0 0 8@1 6 3. c o m。
  • 基金资助:
    军队后勤科研项目( 油2 0 0 8 0 2 0 8) ; 重庆市博士后科研项目( XM 2 0 1 2 0 4 9) 。

Acoustic Emission Signal Recognition Based on Wavelet Transform and RBF Neural Network

  1. (1. Logistics Engineering University,Chongqing 401331, China; 2. 65133 Troop Unit,PLA, Shenyang Liaoning 110043, China)
  • Received:2014-04-01 Revised:2014-09-26 Published:2015-06-25 Online:2015-06-23

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摘要： 声发射检测技术不需开罐就能对储油罐安全性在线评估, 声发射信号识别是储油罐腐蚀状况分析的 基础, 针对现有参数分析法的不足, 提出一种基于小波变换特征提取与 R B F神经网络识别的声发射信号识别方法。 利用d b 2小波对声发射信号6层分解, 将6层细节特征空间的能量作为声发射信号特征向量; 结合声发射信号特点 设计R B F神经网络, 利用已知模式声发射信号训练 R B F网络; 用 R B F神经网络对腐蚀、 裂纹和冷凝声发射信号进 行分类测试。实验结果表明, R B F网络的识别率达到9 3. 3%, 显示了R B F网络识别声发射信号的优越性。对储油罐 安全状况的定量分析具有一定意义。

关键词: 声发射,  , 小波, 细节特征, R B F神经网络, 识别

Abstract: The security condition of oil storage tank can be assessed without opening pot by acoustic emission technology, and acoustic emission signal recognition is the basis of analysis of the corrosion status for oil storage tanks. Against deficiencies of the analysis method by parameters, a new acoustic emission signal recognition method was proposed based on wavelet transform and RBF neural networks. Acoustic emission signal was decomposed to 6 layer by db2 wavelet, and feature vector from acoustic emission signal was composed of the space energy based on 6 layer detail feature. RBF neural network was designed combining the characteristics of acoustic emission signal. The RBF network was trained by using of the acoustic emission signal which pattern has been known.Corrosion, crack and condensation acoustic emission signal were studied by RBF neural network, respectively.The results showed that the recognition rate of RBF neural network reached 93.3%, and the RBF network displayed superiority in identification of the acoustic emission signal, which had a certain significance for quantitative analysis on oil storage tank safety situation.

Key words: Acoustic emission,    ,  Wavelet,    ,  Feature,     ,  RBF neural network,   ,  Recognition

引用本文

周 俊, 王 强, 伊 鸣, 王 帅. 基于小波变换与R B F网络的声发射信号识别[J]. 石油化工高等学校学报, 2015, 28(3): 80-85.

Zhou Jun, Wang Qiang, Yi Ming,Wang Shuai. Acoustic Emission Signal Recognition Based on Wavelet Transform and RBF Neural Network [J]. Journal of Petrochemical Universities, 2015, 28(3): 80-85.

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