Generalized S transform acoustic emission signal processing technology for early fatigue damage evaluation of materials

PDF(2334 KB)

Journal of Vibration and Shock ›› 2020, Vol. 39 ›› Issue (16) : 244-253.

SHI Huiyang1, LI Haiyang1, WANG Zhaoba1, PAN Qianghua2

Author information +

History +

Abstract

In order to evaluate the early fatigue damage degree of materials, a modern signal processing method of the generalized S transform with the modified wide-band Marr wavelet as the kernel function and quantification of information in time-frequency graphs was proposed.First of all, an online acoustic emission detection system for metal fatigue damage was established and acoustic emission signals of early structural fatigue damage of metal materials were collected.Secondly, the generalized S transform was applied to the typical acoustic emission signal to verify that the generalized S transform has higher time-frequency resolution and then the high-resolution time-frequency analyses of acoustic emission signals under high stress and low stress were carried out.Therefore, a more accurate and clear time-frequency graph showing the change of information was obtained with the increase of fatigue cycle number.Finally, a quantization method of information entropy was used to quantify the information entropy of time-frequency graphs with high resolution.The experiment result shows that these signal processing methods can obtain the time-frequency information change characteristics of metal materials in the early structural fatigue damage, and provide a reference basis for acoustic emission detection of commonly used steel fatigue process used in construction engineering and bridge structural components and the aggravation of early fatigue damage degree.

Key words

acoustic emission testing / cyclic fatigue / fatigue damage / generalized S transform / time-frequency analysis / information entropy

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

SHI Huiyang1, LI Haiyang1, WANG Zhaoba1, PAN Qianghua2. Generalized S transform acoustic emission signal processing technology for early fatigue damage evaluation of materials[J]. Journal of Vibration and Shock, 2020, 39(16): 244-253

References

[1] 王丙阳, 耿荣生, 周炳如, 等. 飞机主起落架疲劳试验中的声发射信号处理[J]. 无损检测, 2012, 34(9): 2-6.
WANG Bing-yang, GENG Rong-sheng, ZHOU Bing-ru, et al. Processing of acoustic emission signals in the aircraft main landing gear fatigue test[J]. Nondestructive Testing, 2007, 29(10): 1023.
[2] 彭国平, 张在东, 卢超, 等. Q345R钢拉伸损伤过程声发射特征参数表征及定量评价[J]. 无损检测, 2018, 40(1): 50-54.
PENG Guo-ping, ZHANG Zai-dong, LU Chao, et al. Characterization and quantitative evaluation of tensile damage process of Q345R steel with acoustic emission characteristic parameter[J]. Nondestructive Testing, 2018, 40(1): 50-54.
[3] Aggelis D G, Kordatos E Z, Matikas T E. Acoustic emission for fatigue damage characterization in metal plate. Mechanics Research Communications, 2011, 38(2): 106-110.
[4] 柴孟瑜, 段权, 张早校. Q345R疲劳裂纹扩展过程的声发射研究[J]. 工程科学学报, 2015, 37(12): 1588-1593.
CHAI Meng-yu, DUAN Quan, ZHANG Zao-xiao. Acoustic emission study of fatigue crack propagation in Q345R[J]. Chinese Journal of Engineering, 2015, 37(12): 1588-1593.
[5] 王振京, 胡国华, 喻海荣, et al. 基于声发射技术的Q345B疲劳裂纹扩展研究[J]. 南昌航空大学学报（自然科学版）, 2016(4).
WANG Zhen-jing, HU Guo-hua, YU Hai-rong, et al. Fatigue crack growth of Q345B using acoustic emission technique[J]. Journal of Nanchang Hangkong University: Natural Sciences, 2016(4).
[6] 张一辉, 张文斌, 许飞云, 等.Q235B钢板拉伸损伤试验的声发射特性[J].振动与冲击,2015,34(15):156-161.
ZHANG Yi-hui, ZHANG Wen-bing, XU Fei-yun, et al. Acoustic emission characteristics of Q235B steel plates’ tensile damage tests[J]. Journal of vibration and shock, 2015,34(15):156-161.
[7] Junwen Z. Investigation of Relation between Fracture Scale and Acoustic Emission Time-Frequency Parameters in Rocks[J]. Shock and Vibration, 2018, 2018:1-14.
[8] Morscher, Gregory N. Comprehensive Composite Materials II || 5.12 Nondestructive Evaluation – Use of Acoustic Emission for CMCs[J]. Comprehensive Composite Materials II, 2018:308-324.
[9] 黄振峰, 刘永坚, 毛汉颖, 等. 基于K熵和关联维数的金属疲劳损伤过程的声发射信号特征分析[J].振动与冲击, 2017,36(15):210-214.
HUANG Zhen-feng, LIU Yong-jian, MAO Han-ying, et al. Feature analysis for acoustic emission signals during metal fatigue damage based on Kolmogorov entropy and correlation dimension[J]. Journal of vibration and shock, 2017,36(15):210-214.
[10] 刘婷, 毛汉领, 黄振峰, 等. 金属材料声发射Kaiser效应的混沌特性分析[J]. 振动与冲击, 2017(12).
LIU Ting, MAO Han-ling, HUANG Zhen-feng, et al. Chaotic characteristics of Kaiser effect of metal acoustic emission. Journal of vibration and shock, 2017(12).
[11] Chai M, Zhang Z, Duan Q, et al. Assessment of fatigue crack growth in 316LN stainless steel based on acoustic emission entropy[J]. International Journal of Fatigue, 2018, 109:145-156.
[12] Chai M, Zhang Z, Duan Q. A new qualitative acoustic emission parameter based on Shannon’s entropy for damage monitoring[J]. Mechanical Systems and Signal Processing, 2018, 100:617-629.
[13] 张进, 柴孟瑜, 项靖海, et al. 基于声发射监测的316LN不锈钢的疲劳损伤评价[J]. 工程科学学报, 2018, 40(4): 461-468.
ZHANG Jin, CHAI Meng-yu, XIANG Jing-hai, et al. Fatigue damage evaluation of 316LN stainless steel using acoustic emission monitoring[J]. Chinese Journal of Engineering, 2018, 40(4): 461-468.
[14] 杨浩宇, 骆红云, 陈国伟, et al. 桥式起重机Q345B钢箱形梁母材疲劳损伤的声发射双谱分析[J]. 起重运输机械, 2018.
YANG Hao-yu, LUO Hong-yun, CHENG Guo-wei, et al. Acoustic emission bi-spectrum analysis of fatigue damage of Q345B steel box girder of bridge crane[J]. Hoisting and Conveying Machinery, 2018.
[15] 吕文瀚, 吴先梅, 陈家熠. 金属材料疲劳损伤检测的非线性声学方法[J]. 应用声学, 2018(6).
LIU Wen-han, WU Xian-mei, CHENG Jia-yi. Nonlinear acoustic method for detecting fatigue in metal materials[J]. Journal of Applied Acoustics, 2018(6).
[16] 刘志杰, 袁维祥, 汪元林. 材料早期损伤非线性声学检测研究现状及存在的问题[J]. 科协论坛, 2009(9):99-100.
LIU Zhi-jie, YUAN Wei-xiang, WANG Yuan-ling. Research status and existing problems of nonlinear acoustic detection for early damage of materials[J]. Science & Technology Association Forum, 2009(9):99-100.
[17] 柴孟瑜, 段权, 张早校. 声发射技术在金属疲劳断裂研究中的应用[J]. 化工机械, 2015, 42(6):735-741.
CHAI Meng-yu, DUAN Quan, ZHANG Zao-xiao. Application of acoustic emission technology in study of fatigue fracture of metals[J]. Chemical Engineering & Machinery, 2015, 42(6):735-741.
[18] Stockwell R G, Mansinha L, Lowe R P. Localization of the complex spectrum: the S transform[J]. IEEE Transactions on Signal Processing, 2002, 44(4):998-1001.
[19] 高静怀, 陈文超, 李幼铭, 等. 广义S变换与薄互层地震响应分析[J]. 地球物理学报, 2003, 46(4):526-532.
GAO Jing-huai, CHEN Wen-chao C, LI You-ming, et al. Generalized s transform and seismic response analysis of thin interbeds[J]. Chinese Journal of Geophysics, 2003, 46(4):526-532.
[20] 蔡文涛, 王宗俊, 李绪宣, 等. 基于宽带Marr子波的匹配追踪算法在油气田勘探中的应用[J]. 地球物理学进展, 2014(5):2140-2144.
CAI Wen-tao, WANG Zong-jun, LI Xu-xuan, et al. Application of MP algorithm in oil and gas exploration based on the wide band marr wavelet[J]. Progress in Geophysics, 2014(5):2140-2144.
[21] Shannon C E. A mathematical theory of communication[J]. Bell System Technical Journal, 1948, 27(3):379-423.
[22] Caty O, Buffiere J Y, Maire E, et al. 3D Characterization of the Influence of Porosity on Fatigue Properties of a Cast Al Alloy[J]. Advanced Engineering Materials, 2011, 13(3):194-198.