敲击声学法在钢板孔洞缺陷检测中的应用

摘要
图/表
参考文献(30)
相关文章 (15)

全文: PDF (2445 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要针对目前钢板孔洞缺陷检测方法存在效率低和成本高等不足，研究了敲击声学无损检测方法检测钢板孔洞缺陷的可行性。利用COMSOL Multiphysics仿真软件建立八种含孔洞缺陷钢板的声固耦合模型，进行孔洞缺陷检测仿真，得到无孔和有孔钢板声信号。通过分析声信号时域波形图衰减快慢、功率谱峰值是否单一以及峰值频率大小变化等可以定性识别钢板大孔洞缺陷。对四种工况钢板进行敲击试验，通过仿真与试验声信号功率谱图对比，验证了模拟敲击声场的可行性。将得到的仿真和试验时域信号变换到频域和时频域中，在三种域内共提取45个特征指标进行研究。结果表明，小波包域的能量比累积变异值、能量比偏差和能量比方差三个指标对钢板孔洞缺陷敏感，可以定性地识别钢板孔洞缺陷大小。
关键词：钢板；孔洞缺陷；敲击声学法；小波包

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	聂此槿1
	范书立1
	霍林生1
	孔庆钊2

关键词 ：钢板, 孔洞缺陷, 敲击声学法, 小波包

Abstract：To identify the hole defects of steel plate, the existed detection approaches may cause the problems of low efficiency, costly and etc. Thence, the feasibility of nondestructive detection approach for detecting hole defects based on percussion acoustic was researched. Eight types of acoustic-structure coupling models of steel plates with hole defects were established using COMSOL Multiphysics software. To simulate the detection of hole defects to obtain acoustic signals of non-porous and porous steel plates. By analyzing the decay rate of the acoustic signal in the time domain, the number of power spectrum peaks, and the change of peak frequency, the large hole defects in the steel plate can be qualitatively identified. Percussion tests were performed on steel plates under four types of working conditions, and the feasibility of simulating the percussion sound field is verified by comparing the power spectrum density curves of the simulated and experimental sound signals. Transforming the obtained simulation and test time domain signals into the frequency domain and time-frequency domain, and extracting 45 characteristic indexes in three domains for research. The results show that the three indexes of energy ratio cumulative variation value, energy ratio deviation and energy ratio variance in the wavelet packet domain are sensitive to steel plate hole defects and can qualitatively identify the size of steel plate hole defects.

Key words： steel plate hole defect percussion acoustic approach wavelet packet

收稿日期: 2021-08-09 出版日期: 2022-12-15

引用本文:

聂此槿1，范书立1，霍林生1，孔庆钊2. 敲击声学法在钢板孔洞缺陷检测中的应用[J]. 振动与冲击, 2022, 41(23): 275-282.
NIE Cijin1, FAN Shuli1, HUO Linsheng1, KONG Qingzhao2. Application of percussion acoustic method in detecting steel plate hole defects. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(23): 275-282.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2022/V41/I23/275

[1] PAPAEFTHYMIOU S, TZEVELEKOU T, ANTONOPOULOS A, et al. Typical defects in plate and long steel products[J]. International Journal of Structural Integrity, 2016,7(5): 645-655.
[2] 沈跃, 翟新玲. 钢板孔洞涡流检测信号的频谱分析研究[J]. 无损探伤, 2005(04): 16-18.
Shen Yue, Zhai Xinling. Study on frequency spectrum analysis of eddy current detection signal of steel plate hole[J]. Nondestructive Testing, 2005(04): 16-18.（in Chinese）
[3] 沈跃, 黄延军, 魏淑贤, 等. 小波分析用于钢板孔洞的涡流检测研究[J]. 石油大学学报（自然科学版）, 2005,29(6): 127-130, 150.
Shen Yue, Huang Yanjun, Wei Shuxian, et al. Wavelet analysis for eddy current detection of steel plate holes[J]. Journal of the University of Petroleum (Edition of Natural Science), 2005,29(6): 127-130, 150.（in Chinese）
[4] 常轶民, 金永, 吴靖. 基于光栅投影的钢板孔洞缺陷测量[J]. 光电子激光, 2019,30(10): 1062-1067.
Chang Yimin, Jin Yong, Wu Jing. Steel plate hole defect measurement based on grating projection[J]. Optoelectronics•Laser, 2019,30(10): 1062-1067.（in Chinese）
[5] 汤勃, 戴超凡, 黄文豪. 基于卷积神经网络带标记的钢板表面缺陷检测[J]. 制造业自动化, 2020,42(9): 34-40.
Tang Bo, Dai Chaofan, Huang Wenhao. Marked steel plate surface defect detection based on convolutional neural network[J]. Manufacturing Automation, 2020, 42(9): 34-40.（in Chinese）
[6] WYSOCKA-FOTEK O, MAJ M, OLIFERUK W. Use Of Pulsed IR Thermography For Determination Of Size And Depth Of Subsurface Defect Taking Into Account The Shape Of Its Cross-Section Area[J]. Archives of Metallurgy and Materials, 2015,60(2): 615-620.
[7] ABDULKAREEM M, BAKHARY N, VAFAEI M, et al. Application of two-dimensional wavelet transform to detect damage in steel plate structures[J]. Measurement, 2019,146: 912-923.
[8] CHEN D, MONTANO V, HUO L, et al. Detection of subsurface voids in concrete-filled steel tubular (CFST) structure using percussion approach[J]. Construction and Building Materials, 2020,262: 119761.
[9] 刘蒙. 基于麦克风冲击共振法的钢—混凝土组合结构脱空损伤诊断研究[D]. 长沙: 湖南大学, 2018.
[10] 周云, 裴熠麟, 刘蒙. 基于非接触式麦克风冲击共振测试的钢-混组合结构界面脱空损伤识别方法研究[J]. 地震工程与工程振动, 2020,40(01): 67-79.
Zhou Yun, Pei Yilin, Liu Meng. Research on interface void damage identification method of steel-concrete composite structure based on impact resonance test of non-contact microphone[J]. Journal of Earthquake Engineering and Engineering Vibration, 2020,40(01): 67 -79.
[11] YUAN R, LV Y, KONG Q, et al. Percussion-based bolt looseness monitoring using intrinsic multiscale entropy analysis and BP neural network[J]. Smart materials and structures, 2019,28(12): 125001.
[12] WANG F, HO S C M, SONG G. Modeling and analysis of an impact-acoustic method for bolt looseness identification[J]. Mechanical Systems and Signal Processing, 2019,133: 106249.
[13] WANG F, SONG G. Looseness detection in cup-lock scaffolds using percussion-based method[J]. Automation in Construction, 2020,118: 103266.
[14] ZHENG L, CHENG H, HUO L, et al. Monitor concrete moisture level using percussion and machine learning[J]. Construction and Building Materials, 2019,229: 117077.
[15] GUTHRIE W S, LARSEN J L, BAXTER J S, et al. Automated Air-Coupled Impact-Echo Testing of a Concrete Bridge Deck from a Continuously Moving Platform[J]. Journal of Nondestructive Evaluation, 2019,38(1).
[16] COHEN J I, GORDON-SALANT S. The effect of visual distraction on auditory-visual speech perception by younger and older listeners[J]. The Journal of the Acoustical Society of America, 2017,141(5): L470-L476.
[17] CALADCAD J A, CABAHUG S, CATAMCO M R, et al. Determining Philippine coconut maturity level using machine learning algorithms based on acoustic signal[J]. Computers and electronics in agriculture, 2020,172: 105327.
[18] ZHANG Y, DENG X, XU Z, et al. Watermelon Ripeness Detection via Extreme Learning Machine with Kernel Principal Component Analysis Based on Acoustic Signals[J]. International Journal of Pattern Recognition and Artificial Intelligence, 2019,33(8): 1951001-1951002.
[19] TONG F, TSO S K, XU X M. Tile-wall bonding integrity inspection based on time-domain features of impact acoustics[J]. Sensors and Actuators A: Physical, 2006,132(2): 557-566.
[20] TONG F, XU X M, LUK B L, et al. Evaluation of tile–wall bonding integrity based on impact acoustics and support vector machine[J]. Sensors and Actuators A: Physical, 2008,144(1): 97-104.
[21] XING T, BAI Y, FANG Y. Study on detecting lumber damage using acoustic radiation[J]. Procedia Engineering, 2010,7: 410-415.
[22] 唐伟. 声学缺陷检测中关键技术的研究[D]. 天津: 天津大学, 2018.
[23] 丁幼亮, 李爱群, 缪长青. 基于小波包能量谱的结构损伤预警方法研究[J]. 工程力学, 2006,23(8): 42-48.
Ding Youliang, Li Aiqun, Miao Changqing. Research on structural damage warning method based on wavelet packet energy spectrum[J]. Engineering Mechanics, 2006,23(8): 42-48.（in Chinese）
[24] 葛继平, 李胡生. 基于小波包能量累积变异的梁桥损伤识别试验[J]. 振动.测试与诊断, 2011,31(01): 85-88.
Ge Jiping, Li Husheng. Beam bridge damage identification test based on wavelet packet energy accumulation variation[J]. Journal of Vibration, Measurement & Diagnosis, 2011, 31(01): 85-88.（in Chinese）
[25] 刘习军,商开然,张素侠,等.基于改进小波包能量的梁式结构损伤识别[J].振动与冲击,2016,35(13):179-185+200.
Liu Xijun, Shang Kairan, Zhang Suxia, et al. Damage detection method for beam structures based on improved Wavelet Packet energy[J].Journal of Vibration and Shock,2016,35(13):179-185+200.（in Chinese）
[26] 蒋佳炜,胡以怀,柯赟,陈彦臻.基于小波包特征提取和模糊熵特征选择的柴油机故障分析[J].振动与冲击,2020,39(04):273-277+298.
Jiang Jiawei, Hu Yihuai, Ke Yun, et al. Fault diagnosis of diesel engines based on wavelet packet energy spectrum feature extraction and fuzzy entropy feature selection[J]. Journal of Vibration and Shock,2020,39(04):273-277+298.（in Chinese）
[27] 张涛, 高新意, 唐伟, 等. 基于神经网络的玻璃缺陷声学检测方法[J]. 声学技术, 2018,37(05): 488-495.
Zhang Tao, Gao Xinyi, Tang Wei, et al. Acoustic detection method for glass defects based on neural network[J]. Technical Acoustics, 2018,37(05): 488-495.（in Chinese）
[28] 杜功焕, 朱哲民, 龚秀芬. 声学基础[M]. 第3版. 南京: 南京大学出版社, 2012: 107-123.
[29] 王勖成. 有限单元法[M]. 北京: 清华大学出版社, 2003: 13-93.
[30] 何琳, 朱海潮, 邱小军, 等. 声学理论与工程应用[M]. 北京: 科学出版社, 2006: 230-231.