STUDY OF TANK ACOUSTIC EMISSION TESTING SIGNALS ANALYSIS METHOD BASED ON WAVELET NEURAL NETWORK

被引：0
|
作者：
Li Wei [1 ]
Dai Guang [1 ]
Long Feifei [1 ]
Wang Yali [1 ]
机构：
[1] Northeast Petr Univ, Daqing 163318, Heilongjiang Pr, Peoples R China
来源：
PROCEEDINGS OF THE ASME PRESSURE VESSELS AND PIPING CONFERENCE VOL 1: CODES AND STANDARDS | 2012年
关键词：
storage tank; acoustic emission; wavelet neural network; signal processing;
D O I：
暂无
中图分类号：
TH [机械、仪表工业];
学科分类号：
0802 ;
摘要：
Acoustic emission technology is mostly used in corrosion detection of the atmospheric vertical storage tank bottom, but the evaluation results are always affected by the complex sound sources. In this paper, wavelet neural network is used to identify the acoustic emission signals from different types of tanks. Using wavelet transform and threshold denoising to denoise the detection signals, after wavelet packet decomposition, each node's energy distribution and the feature vectors of extracted corrosion signals of the tank floor are selected as the input. At last, the compact-type wavelet neural network is chosen to recognize different AE signals. The result of magnetic flux leakage test proves that this method can improve acoustic emission signal analysis precision and achieve the accurate corrosion evaluation based on AE technology.
引用
收藏
页码：699 / 703
页数：5
相关论文
共 50 条
  • [41] Experimental study on train axle fatigue crack acoustic emission signals recognition based on a one-dimensional convolutional neural network
    Lin, Li
    Zhou, Kanghui
    Li, Daguang
    Li, Kun
    Wang, Xinyu
    NONDESTRUCTIVE TESTING AND EVALUATION, 2024,
  • [42] Automated analysis of acoustic emission signals based on the ISODATA algorithm
    Angelopoulos, N.
    Papaelias, M.
    INSIGHT, 2018, 60 (03) : 130 - 138
  • [43] In-process monitoring of drilling burr formation using acoustic emission and a wavelet-based artificial neural network
    Lee, S. H.
    Lee, D.
    INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2008, 46 (17) : 4871 - 4888
  • [44] Analysis of acoustic emission signals in machining
    Bukkapatnam, STS
    Kumara, SRT
    Lakhtakia, A
    6TH INDUSTRIAL ENGINEERING RESEARCH CONFERENCE PROCEEDINGS: (IERC), 1997, : 450 - 455
  • [45] Best wavelet basis for wavelet transforms in acoustic emission signals of concrete damage process
    Wang, Y.
    Chen, S. J.
    Liu, S. J.
    Hu, H. X.
    RUSSIAN JOURNAL OF NONDESTRUCTIVE TESTING, 2016, 52 (03) : 125 - 133
  • [46] Best wavelet basis for wavelet transforms in acoustic emission signals of concrete damage process
    Y. Wang
    S. J. Chen
    S. J. Liu
    H. X. Hu
    Russian Journal of Nondestructive Testing, 2016, 52 : 125 - 133
  • [47] The study of soft sensor modeling method based on wavelet neural network for sewage treatment
    Gao, Mei-Juan
    Tian, Jing-Wen
    Li, Kai
    2007 INTERNATIONAL CONFERENCE ON WAVELET ANALYSIS AND PATTERN RECOGNITION, VOLS 1-4, PROCEEDINGS, 2007, : 721 - +
  • [48] A Power Harmonic Detection Method Based on Wavelet Neural Network
    Wang, Jinyu
    Sun, Tao
    Zhao, Yongzhong
    Wang, Chao
    PROCEEDINGS OF 2013 2ND INTERNATIONAL CONFERENCE ON MEASUREMENT, INFORMATION AND CONTROL (ICMIC 2013), VOLS 1 & 2, 2013, : 1148 - 1152
  • [49] Water quality forecasting method based on wavelet neural network
    Chen Jian-qiu
    Zhang Xin-zheng
    Proceedings of 2006 Chinese Control and Decision Conference, 2006, : 723 - 726
  • [50] Damage Evolution During Rock Dynamic Compression Revealed by Wavelet Analysis of Acoustic Emission Signals
    Yang, Ling
    Wu, Bangbiao
    Xu, Ying
    Fu, Yan
    Xia, Kaiwen
    ROCK MECHANICS AND ROCK ENGINEERING, 2024, 57 (02) : 1527 - 1535
12345