中国安全科学学报 ›› 2019, Vol. 29 ›› Issue (6): 158-164.doi: 10.16265/j.cnki.issn1003-3033.2019.06.026

• 公共安全 • 上一篇    下一篇

面向储罐声发射检测的信号分析技术研究

沈书乾1,2 高级工程师, 李伟**1 教授   

  1. 1 东北石油大学 机械科学与工程学院,黑龙江 大庆 163318;
    2 广东省特种设备检测研究院 茂名检测院,广东 茂名 525000
  • 收稿日期:2019-02-26 修回日期:2019-04-02 发布日期:2020-11-02
  • 通讯作者: **李伟(1970—),男,黑龙江大庆人,博士,教授,主要从事声发射在线检测技术研究。E-mail:ssq7980@163.com。
  • 作者简介:沈书乾(1979—),男,江西九江人,博士研究生,高级工程师,研究方向为特种设备的检验检测方法。E-mail:ssq7980@163.com。

Study on signal analysis technology of tank acoustic emission testing

SHEN Shuqian1,2, LI Wei1   

  1. 1 College of Mechanical Science and Engineering, Northeast Petroleum University, Daqing Heilongjiang 163318, China;
    2 Maoming Inspection Institute, Guangdong Special Equipment Inspection and Research Institute, Maoming Guangdong 525000, China
  • Received:2019-02-26 Revised:2019-04-02 Published:2020-11-02

摘要: 为提高非专家检测人员执行储罐声发射检测后评价的准确度,综合分析检测过程中常见的噪声类型,提出基于小波分析和模式识别的声发射信号处理方法;设计Q235B板三点弯曲、砂纸打磨、橡胶手柄敲击和电噪声试验,并收集新声发射波形特征量分析试验数据,获取不同类信号特征;结合提出的信号处理方法和新特征量,聚类分析大样本数据以区分不同类型声源。结果表明:所提方法可以定量反映声发射波形的突发性,能够解决声发射信号类型的识别问题;该方法还可提取有效声发射信号典型特征,并保留关键频带小波系数来重构波形,适用于储罐的声发射检测。

关键词: Q235B, 声发射, 小波分析, 储罐, 聚类分析

Abstract: In order to improve the accuracy of post-test evaluation of tanks by non-expert inspectors and comprehensively analyze those common types of noise, an acoustic emission signal processing method based on wavelet analysis and pattern recognition was proposed The experiment of Q235B three-point bending sandpaper rub, rubber handle percussion and electric noise was designed, and the experimental data of new acoustic emission waveform feature analysis were collected to obtain different kinds of signal characteristics. Finally, the large sample data was cluster analyzed to distinguish different types of sound sources based on the proposed signal processing method and the new feature quantity. The results show that the proposed method can quantitatively reflect the sudden burst of acoustic emission waveforms and identify the signal type of acoustic emission; it is also found to be able to extract typical features of valid acoustic emission signals and preserve key frequency band wavelet coefficients to reconstruct waveforms, thus making it suitable for acoustic emission testing of tanks.

Key words: Q235B, acoustic emission, wavelet analysis, storage tank, cluster analysis

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