摘要
超声漏表面波可用于检测表面或近表面缺陷,其非接触检测的优点易于实现自动化检测。但由于波型转换与传播衰减,漏表面波的回波幅值较小,不利于缺陷检测和成像。仿真分析了漏表面波的传播特性及缺陷回波特征,应用主成分分析分离回波信号中的干扰波,再利用小波域隐马尔可夫模型算法分离整段信号的系统噪声,联合两种方法提取漏表面波信号中的缺陷信息,最后通过频域合成孔径算法对漏表面波扫查数据进行了高分辨率图像重建。结果表明,相比于传统B扫成像,基于PCA-WHMM的超声漏表面波F-SAFT方法在回波信号平均信噪比上提高了10.05dB,平均成像误差降低了26.3%,为金属表面及近表面缺陷检测提供了一种有效方法。
Ultrasonic leaky Rayleigh waves can be used to detect surface and sub-surface defects. The advantage of non-contact detection makes it easy to realize automatic inspection. However, due to the waveform conversion and propagation attenuation, the echo amplitude of leaky Rayleigh waves is quite small, which is not conducive for defect detection and imaging. It simulates and analyzes the propagation characteristics and defect echo characteristics of the leaky Rayleigh waves, applies principal component analysis(PCA) to separate the interference waves from the echo signal, then uses the Wavelet-based hidden Markov models(WHMM) algorithm to separate the systematic noise from the whole signal, combines the two methods to extract the defect information in the leaky Rayleigh waves signal, and finally uses the frequency-domain synthetic aperture focusing technology(F-SAFT) to conduct a high resolution ratio reconstruction for the data of the leaky Rayleigh waves scanning. The results show that compared with the conventional B-scan imaging, the PCA-WHMM-based ultrasonic leaky Rayleigh waves F-SAFT imaging method improves the SNR of the echo signal by 10.05 dB and reduces the average imaging error by 26.3%,which provides an effective method for the detection of metal surface and sub-surface defects.
作者
胡宏伟
周刚
沈晓炜
徐晓强
HU Hongwei;ZHOU Gang;SHEN Xiaowei;XU Xiaoqiang(College of Automotive and Mechanical Engineering,Changsha University of Science and Technology,Changsha 410114;Hunan Provincial Key Laboratory of Intelligent Manufacturing Technology for High-Performance Mechanical Equipment,Changsha 410114)
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2021年第14期177-187,共11页
Journal of Mechanical Engineering
基金
国家自然科学基金(52075049)
湖南省自然科学杰出青年基金(2020JJ2028)资助项目。
关键词
超声无损检测
漏表面波
主成分分析
小波域隐马尔可夫模型
频域合成孔径
ultrasonic nondestructive testing
leaky Rayleigh waves
principal component analysis
wavelet-based hidden Markov models
frequency-domain synthetic aperture focusing
