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High resolution non-destructive evaluation of defects using artificial neural networks and wavelets

机译:使用人工神经网络和小波对缺陷进行高分辨率无损评估

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摘要

This paper presents artificial neural networks (ANN) and wavelet analysis as methods that can assist high resolution of multiple defects in close proximity in components. Without careful attention to analysis, multiple defects can be mis-interpreted as single defects and with the possibility of significantly underestimated sizes. The analysis in this work focussed on A-scan type ultrasonic signal. Amplitudes corresponding to the sizes of two defects as well as the phase shift parameter representing the distance between them were determined. The results obtained demonstrate very good correlation for sizes and distances respectively even in cases involving noisy signal data.
机译:本文介绍了人工神经网络(ANN)和小波分析作为可以帮助高分辨率解决组件中多个缺陷的方法。如果不注意分析,多个缺陷可能会被误解为单个缺陷,并且可能会严重低估尺寸。这项工作的分析集中在A扫描型超声信号上。确定与两个缺陷的大小相对应的振幅以及代表它们之间的距离的相移参数。即使在涉及嘈杂信号数据的情况下,所获得的结果也分别显示出大小和距离的良好关联。

著录项

  • 来源
    《NDT & E international》 |2012年第2012期|p.69-75|共7页
  • 作者单位

    Oxford Brookes University, Faculty of Technology, Design and Environment, Wheatley Campus, Oxford 0X33 1HX, UK;

    Oxford Brookes University, Faculty of Technology, Design and Environment, Wheatley Campus, Oxford 0X33 1HX, UK;

    Oxford Brookes University, Faculty of Technology, Design and Environment, Wheatley Campus, Oxford 0X33 1HX, UK;

    Institute Politecnico Nacional, ESIME SEP1 Edificio 5, 3er piso, Unidad Profesional Adolfo Lopez Mateos, colonia Lindavista, Gustavo A Madero, 07738 Mexico, D. F. Mexico;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    multi defects; ultrasonic; artificial neural networks; wavelets; non-destructive evaluation;

    机译:多重缺陷超声波人工神经网络;小波无损评估;

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