Acoustic emission localization technique based on fiber Bragg grating sensing network and signal feature reconstruction

Zhang Fa-Ye; Jiang Ming-Shun; Sui Qing-Mei; Lü Shan-Shan; Shan Jia

doi:10.7498/aps.66.074210

Abstract

Based on the analysis of the propagation mechanism of acoustic emission stress waves caused by impact excitation on aluminum alloy plates, The geometrical model of steel ball impacted aluminum alloy plate was built by ABAQUS software, and the stress wave propagation process is simulated and analyzed. The stress wave propagation process is simulated and analyzed and an acoustic emission sensing system based on the principle of edge filtering is constructed. The acoustic emission stress waves were collected to establish the acoustic emission region localization model. The localization method of acoustic emission region based on diffusion mapping and support vector machine is proposed and verified experimentally. Multiple acoustic emission localization experiments were performed on an aluminum alloy plate of 300 mm300 mm2 mm, which was divided into 36 test area. The results show that the localization accuracy is 30 mm30 mm and the positioning accuracy was 97.5%, while consuming 0.781 s. The study provides an effective method for acoustic emission localization.

Keywords:

Authors and contacts

1.
School of Control Science and Engineering, Shandong University, Jinan 250061, China

Corresponding author: Sui Qing-Mei, jiangmingshun@sdu.edu.cn

Funds: Project supported by the National Natural Science Foundation of China (Grant No. 41472260), the Natural Science Foundation of Shandong Province, China (Grant No. ZR2014FM025), the Fundamental Research Funds of Shandong University, China (Grant No. 2016JC012), and the Young Scholars Program of Shandong University, China (Grant No. 2016WLJH30).

References

[1]	Zhang J R, Ma H Y, Yan W J, Li Z J 2016 Appl. Acoust. 105 67
[2]	Loutas T H, Panopoulou A, Roulias D, Kostopoulos V 2012 Expert Syst. Appl. 39 8412
[3]	Wiggins S M, Leifer I, Linke P, Hildebrand J A 2015 Marine and Petroleum Geology 68 776
[4]	Fu T 2014 Ph. D. Dissertation (Haerbin:Harbin Institute of Technology) (in Chinese)[付涛2014博士学位论文(哈尔滨:哈尔滨工业大学)]
[5]	Yu F M, Okabe Y, Wu Q, Shigeta N 2016 Composites Sci. Technol. 135 116
[6]	Gao X, Zhang X P, Li N, Xin P 2014 Asia-Pacific International Symposium on Aerospace Technology China September 24-36, 2014 p1203
[7]	Munoz V, Vales B, Perrin M, Pastor M, Welmane H, Cantarel A, Karama M 2016 Composites Part B 85 68
[8]	Shrestha P, Kim J, Park Y, Kim C 2015 Composite Struct. 125 159
[9]	Hafizi Z, Epaarachchi J, Lau K 2015 Measurement 61 51
[10]	Jiang M S, Sui Q M, Jia L, Peng P, Cao Y Q 2012 Optoelectron. Lett. 8 220
[11]	Cheng X M, Zhang X D, Zhao L, Deng A D, Bao Y Q, Liu Y, Jiang Y L 2014 Comptes Rendus Mecanique 342 229
[12]	Jiang Y, Xu F Y, Xu B S 2015 Mechanical Systems and Signal Processing 64 452
[13]	Sadegh H, Mehdi A, Mehdi A 2016 Tribology International 95 426
[14]	Li Z B, Ma H L, Cao Z S, Sun M G, Huang Y B, Zhu W Y, Liu Q 2016 Acta Phys. Sin. 65 053301 (in Chinese)[李志彬, 马宏亮, 曹振松, 孙明国, 黄印博, 朱文越, 刘强2016 65 053301]
[15]	Jin Z W 2014 M. S. Dissertation (Shandong:Shandong University) (in Chinese)[金中薇2014硕士学位论文(山东:山东大学)]
[16]	Cao Y, Pei Y W, Tong Z R 2014 Acta Phys. Sin. 63 024206(in Chinese) (in Chinese)[曹晔, 裴庸惟, 童峥嵘2014 63 024206]
[17]	Gu B, Sun X, Sheng V S 2016 IEEE Trans. Neural Networks Learning Systems 1 1
[18]	Scholkopf B, Smola A, Williamson R, Bartlett 2000 Neural Comput. 15 1207
[19]	Lafon S, Lee A B 2006 IEEE Trans. Pattern Anal. Machine Intellig. 28 1393
[20]	Nadler B, Lafon S, Coifman R R, Kevrekidis I G 2006 Appl. Computat. Harmonic Anal. 21 113
[21]	Jia B, Yu B T, Wu Q, Yang X S, Wei C F, Law R, Fu S 2016 Neurocomputing 189 106

Cited By

[1]	Zhang J R, Ma H Y, Yan W J, Li Z J 2016 Appl. Acoust. 105 67
[2]	Loutas T H, Panopoulou A, Roulias D, Kostopoulos V 2012 Expert Syst. Appl. 39 8412
[3]	Wiggins S M, Leifer I, Linke P, Hildebrand J A 2015 Marine and Petroleum Geology 68 776
[4]	Fu T 2014 Ph. D. Dissertation (Haerbin:Harbin Institute of Technology) (in Chinese)[付涛2014博士学位论文(哈尔滨:哈尔滨工业大学)]
[5]	Yu F M, Okabe Y, Wu Q, Shigeta N 2016 Composites Sci. Technol. 135 116
[6]	Gao X, Zhang X P, Li N, Xin P 2014 Asia-Pacific International Symposium on Aerospace Technology China September 24-36, 2014 p1203
[7]	Munoz V, Vales B, Perrin M, Pastor M, Welmane H, Cantarel A, Karama M 2016 Composites Part B 85 68
[8]	Shrestha P, Kim J, Park Y, Kim C 2015 Composite Struct. 125 159
[9]	Hafizi Z, Epaarachchi J, Lau K 2015 Measurement 61 51
[10]	Jiang M S, Sui Q M, Jia L, Peng P, Cao Y Q 2012 Optoelectron. Lett. 8 220
[11]	Cheng X M, Zhang X D, Zhao L, Deng A D, Bao Y Q, Liu Y, Jiang Y L 2014 Comptes Rendus Mecanique 342 229
[12]	Jiang Y, Xu F Y, Xu B S 2015 Mechanical Systems and Signal Processing 64 452
[13]	Sadegh H, Mehdi A, Mehdi A 2016 Tribology International 95 426
[14]	Li Z B, Ma H L, Cao Z S, Sun M G, Huang Y B, Zhu W Y, Liu Q 2016 Acta Phys. Sin. 65 053301 (in Chinese)[李志彬, 马宏亮, 曹振松, 孙明国, 黄印博, 朱文越, 刘强2016 65 053301]
[15]	Jin Z W 2014 M. S. Dissertation (Shandong:Shandong University) (in Chinese)[金中薇2014硕士学位论文(山东:山东大学)]
[16]	Cao Y, Pei Y W, Tong Z R 2014 Acta Phys. Sin. 63 024206(in Chinese) (in Chinese)[曹晔, 裴庸惟, 童峥嵘2014 63 024206]
[17]	Gu B, Sun X, Sheng V S 2016 IEEE Trans. Neural Networks Learning Systems 1 1
[18]	Scholkopf B, Smola A, Williamson R, Bartlett 2000 Neural Comput. 15 1207
[19]	Lafon S, Lee A B 2006 IEEE Trans. Pattern Anal. Machine Intellig. 28 1393
[20]	Nadler B, Lafon S, Coifman R R, Kevrekidis I G 2006 Appl. Computat. Harmonic Anal. 21 113
[21]	Jia B, Yu B T, Wu Q, Yang X S, Wei C F, Law R, Fu S 2016 Neurocomputing 189 106

[1]	Liang Ke-Da, Liu Teng-Fei, Chang Zhe, Zhang Meng, Li Zhi-Xin, Huang Song-Song, Wang Jing. Inversion models of internal solitary wave propagation speed in ocean based on least squares method and support vector machine. Acta Physica Sinica, 2023, 72(2): 028301. doi: 10.7498/aps.72.20221633
[2]	Zhang Yi-Jun, Mu Xiao-Dong, Guo Le-Meng, Zhang Peng, Zhao Dao, Bai Wen-Hua. A support vector machine training scheme based on quantum circuits. Acta Physica Sinica, 2023, 72(7): 070302. doi: 10.7498/aps.72.20222003
[3]	Song Kun, Gao Tai-Chang, Liu Xi-Chuan, Yin Min, Xue Yang. Method and experiment of rainfall intensity inversion using a microwave link based on support vector machine. Acta Physica Sinica, 2015, 64(24): 244301. doi: 10.7498/aps.64.244301
[4]	Zhao Zhi-Gang, Zhang Chun-Jie, Gou Xiang-Feng, Sang Hu-Tang. Solar cell temperature prediction model of support vector machine optimized by particle swarm optimization algorithm. Acta Physica Sinica, 2015, 64(8): 088801. doi: 10.7498/aps.64.088801
[5]	Meng Qing-Fang, Chen Shan-Shan, Chen Yue-Hui, Feng Zhi-Quan. Automatic detection of epileptic EEG based on recurrence quantification analysis and SVM. Acta Physica Sinica, 2014, 63(5): 050506. doi: 10.7498/aps.63.050506
[6]	Yu Yang, Hao Zhong-Qi, Li Chang-Mao, Guo Lian-Bo, Li Kuo-Hu, Zeng Qing-Dong, Li Xiang-You, Ren Zhao, Zeng Xiao-Yan. Identification of plastics by laser-induced breakdown spectroscopy combined with support vector machine algorithm. Acta Physica Sinica, 2013, 62(21): 215201. doi: 10.7498/aps.62.215201
[7]	Zhao Yong-Ping, Zhang Li-Yan, Li De-Cai, Wang Li-Feng, Jiang Hong-Zhang. Chaotic time series prediction using filtering window based least squares support vector regression. Acta Physica Sinica, 2013, 62(12): 120511. doi: 10.7498/aps.62.120511
[8]	Xing HongYan, Qi ZhengDong, Xu Wei. Weak signal estimation in chaotic clutter using selective support vector machine ensemble. Acta Physica Sinica, 2012, 61(24): 240504. doi: 10.7498/aps.61.240504
[9]	Wang Fang-Fang, Zhang Ye-Rong. An electromagnetic inverse scattering approach based on support vector machine. Acta Physica Sinica, 2012, 61(8): 084101. doi: 10.7498/aps.61.084101
[10]	Yan Xiao-Mei, Liu Ding. Control of fractional order chaotic system based on least square support vector machines. Acta Physica Sinica, 2010, 59(5): 3043-3048. doi: 10.7498/aps.59.3043
[11]	Wang Ge-Li, Yang Pei-Cai, Mao Yu-Qing. On the application of non-stationary time series prediction based on the SVM method. Acta Physica Sinica, 2008, 57(2): 714-719. doi: 10.7498/aps.57.714
[12]	. Prediction of chaotic time series based on selective support vector machine ensemble. Acta Physica Sinica, 2007, 56(12): 6820-6827. doi: 10.7498/aps.56.6820
[13]	Zhang Jia-Shu, Dang Jian-Liang, Li Heng-Chao. Local support vector machine prediction of spatiotemporal chaotic time series. Acta Physica Sinica, 2007, 56(1): 67-77. doi: 10.7498/aps.56.67
[14]	Zhu Tao, Rao Yun-Jiang, Mo Qiu-Ju. A high sensitivity fiber-optic torsion sensor based on a novel ultra long-period fiber grating. Acta Physica Sinica, 2006, 55(1): 249-253. doi: 10.7498/aps.55.249
[15]	Ye Mei-Ying. Control of chaotic system based on least squares support vector machine modeling. Acta Physica Sinica, 2005, 54(1): 30-34. doi: 10.7498/aps.54.30
[16]	Ye Mei-Ying, Wang Xiao-Dong, Zhang Hao-Ran. Chaotic time series forecasting using online least squares support vector machine regression. Acta Physica Sinica, 2005, 54(6): 2568-2573. doi: 10.7498/aps.54.2568
[17]	Liu Han, Liu Ding, Ren Hai-Peng. Chaos control based on least square support vector machines. Acta Physica Sinica, 2005, 54(9): 4019-4025. doi: 10.7498/aps.54.4019
[18]	Cui Wan-Zhao, Zhu Chang-Chun, Bao Wen-Xing, Liu Jun-Hua. Prediction of the chaotic time series using support vector machines for fuzzy rule-based modeling. Acta Physica Sinica, 2005, 54(7): 3009-3018. doi: 10.7498/aps.54.3009
[19]	Cui Wan-Zhao, Zhu Chang-Chun, Bao Wen-Xing, Liu Jun-Hua. Prediction of the chaotic time series using support vector machines. Acta Physica Sinica, 2004, 53(10): 3303-3310. doi: 10.7498/aps.53.3303
[20]	Qiao Xue-Guang, Jia Zhen-An, Fu Hai-Wei, Li Ming, Zhou Hong. Theory and experiment about in-fiber Bragg grating temperature sensing. Acta Physica Sinica, 2004, 53(2): 494-497. doi: 10.7498/aps.53.494

Catalog

Vol. 66, Issue 7 - 2017-04-05

Metrics

Abstract views: 6954
PDF Downloads: 284
Cited By: 0

姓名
邮箱
手机号码
标题
留言内容
验证码

Search

Article

留言板