Application of improved threshold denoising based on wavelet transform to ultrasonic signal processing
-
摘要: 超声检测中回波信号信噪比低、易于被噪声淹没,小波变换是一种有效的提取缺陷回波的方法.建立了超声缺陷回波信号的数学模型,对基于小波变换的软、硬阈值消噪法作了改进,提出一种折中方法用于超声缺陷回波信号的去噪,同时以信噪比为目标函数对参数的选取也作了优化.仿真实验结果表明,改进方法非常适合用于超声信号的分析,能够很好地抑制噪声,它最大程度的发挥了小波软、硬阈值消噪法的优点,避免它们的缺点,使用该方法处理的信号相对于小波软、硬阈值消噪法在一定程度上改善了去噪的效果,提高了回波信号的信噪比.Abstract: The signal to noise ratio of ultrasonic echoes signal was low, the echoes signal was submerged easily in ultrasonic testing, and wavelet transforms was an effective method by which the flaw echoes can be extracted. The mathematics model of ultrasonic echoes signal was established, including the flaw echoes and the noise, the traditional soft and hard threshold denoising methods based on wavelet transform was ameliorated, and a middle course method was put forward for signal denoising in ultrasonic testing. At the same time the parameters selection was also optimized with the signal to noise ratio of ultrasonic flaw echoes signal as object function. The simulation experimental results showed that this method was fit for analyzing ultrasonic signal, and it can depress noises well. This method utilized the advantage of the soft and hard threshold denoising methods and avoided their disadvantage in the farthest. Compare to the traditional soft and hard threshold denoising methods, the denoising effect was improved in a certain extent, and the signal to noise ratio of ultrasonic flaw echoes signal was improved by using this method.
-
Key words:
- wavelet transforms /
- ultrasonic testing /
- signal to noise ratio /
- signal processing
-
[1] 崔锦泰. 小波分析导论[M]. 西安:西安交通大学出版社, 1995 Cui Jintai. Conspectus of wavelet analysis[M]. Xi’an:Xi’an Jiaotong University Press, 1995(in Chinese) [2] Mallat S. A theory for multiresolution signal decomposition:the wavelet representation[J]. IEEE Trans Pattern Anal and Machine Intell, 1989, 11(7):647~693 [3] Gustafsson M G, Stepinski T. Split spectrum algorithms rely on instantaneous phase information—a geometrical approach[J]. IEEE Trans UFFC, 1993, 40(6):659~665 [4] 张广明. 超声无损检测中的时频分析理论及应用研究 . 西安:西安交通大学机械工程学院, 1999 Zhang Guangming. The theory and application research of time-frequency analysis in ultrasonic nondestructive evaluation . Xi’an:School of Mechanical Engineering, Xi’an Jiaotong University, 1999(in Chinese) [5] Mallat S, Hwang W L. Singularity detection and processing with wavelets[J]. IEEE Trans on Information Theory, 1992, 38(2):617~643 [6] 范 中. 利用子波变换检测瞬时信号[J]. 电子学报, 1996, 24(1):79~82 Fan Zhong. Detect instantaneous signals using wavelet transform[J]. Journal of Electron, 1996, 24(1):79~82(in Chinese) [7] Donoho D L, Johnstone I M. Ideal spatial adaption by wavelet shrinkage[J]. Biometrika, 1994, 81:425~455 [8] Donoho D L, Johnstone I M. Adapting to unknown smoothness via wavelet shrinkage[J]. Journal of the American Statistical Association, 1995, 90:1200~ 1224 期刊类型引用(8)
1. 徐红,矫桂娥,张文俊,陈一民. 基于卷积神经网络的结构化非平衡数据分类算法. 计算机工程. 2023(02): 81-89 . 百度学术
2. 王萌铎,续欣莹,阎高伟,史丽娟,郭磊. 基于AdaBoost集成加权宽度学习系统的不平衡数据分类. 计算机工程. 2022(04): 99-105+112 . 百度学术
3. 张利剑,陈晋鹏. 基于扩展Jarvis-Patrick聚类的异常检测算法优化及检测仿真. 电子设计工程. 2022(13): 100-104 . 百度学术
4. 张伊扬,钱育蓉,陶文彬,冷洪勇,李自臣,马梦楠. 基于深度学习的属性图异常检测综述. 计算机工程与应用. 2022(19): 1-13 . 百度学术
5. 苏江军,董一鸿,颜铭江,钱江波,辛宇. 面向复杂网络的异常检测研究进展. 控制与决策. 2021(06): 1293-1310 . 百度学术
6. 陈波冯,李靖东,卢兴见,沙朝锋,王晓玲,张吉. 基于深度学习的图异常检测技术综述. 计算机研究与发展. 2021(07): 1436-1455 . 百度学术
7. 张建宁. 基于改进动态图算法的软件保护技术. 科技通报. 2021(08): 56-60 . 百度学术
8. 吴德胜,管媛辉. 移动互联网异常入侵行为下攻击意图预测仿真. 计算机仿真. 2018(12): 241-244 . 百度学术
其他类型引用(5)
-

计量
- 文章访问数: 3126
- HTML全文浏览量: 188
- PDF下载量: 946
- 被引次数: 13