Determination of POD of AE wave source based on phononic crystals

LI Hongyu, ZENG Xiangxing, ZHANG Lu

Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (7) : 75-83.

PDF(2853 KB)
PDF(2853 KB)
Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (7) : 75-83.

Determination of POD of AE wave source based on phononic crystals

  • LI Hongyu, ZENG Xiangxing, ZHANG Lu
Author information +
History +

Abstract

Acoustic emission testing (AE) is a nondestructive method for evaluating material status that involves the acquisition and analysis of signals produced by damage. However, the unknown AE source during testing poses a challenge to the study of probability of detection (POD). Moreover, the reliability of the detection results when utilizing acoustic emission to identify interior material degradation can be directly influenced by the acoustic emission source, making the AE source's detection probability a crucial consideration in acoustic emission detection. In this study, we use the features of phononic crystal to form a physical stopband to filter out the noise signals. Then the acoustic emission signal due to the crack growth of aluminum metal compact tensile specimens under cyclic loading can be obtained. To further explore the influence of threshold level and systematic filter frequency on the POD, the parametric study was conducted. The AE source related POD was estimated experimentally, and the POD increases with the growth of fatigue cracks, herein, the peak POD happens in the process of cracking other than the end stage. In addition, with the proper selection of the threshold and filters, the AE results can be improved more reliable. Therefore, it is essential to assess the POD for the AE method.

Key words

acoustic emission (AE) / probability of detection (POD) / phononic crystal / fatigue cracking

Cite this article

Download Citations
LI Hongyu, ZENG Xiangxing, ZHANG Lu. Determination of POD of AE wave source based on phononic crystals[J]. Journal of Vibration and Shock, 2024, 43(7): 75-83

References

[1] Elfergani H A, Pullin R, Holford K M. Damage assessment of corrosion in prestressed concrete by acoustic emission[J]. Construction and Building Materials, 2013, 40: 925-933. [2] 郝如江,卢文秀,褚福磊. 声发射检测技术用于滚动轴承故障诊断的研究综述[J]. 振动与冲击,2008(03):75-79+181. Hao, Ru-jiang, Lu, Wen-xiu, Chu, Fu-lei. Research review of acoustic emission detection technology for rolling bearing fault diagnosis[J]. Journal of Vibration and Shock, 2008(03):75-79+181. [3] Guo Y B, Ammula S C. Real-time acoustic emission monitoring for surface damage in hard machining[J]. International Journal of Machine Tools and Manufacture, 2005, 45(14): 1622-1627. [4] Van Hieu B, Choi S, Kim Y U, et al. Wireless transmission of acoustic emission signals for real-time monitoring of leakage in underground pipes[J]. KSCE Journal of Civil Engineering, 2011, 15: 805-812. [5] Maillet E, Baker C, Morscher G N, et al. Feasibility and limitations of damage identification in composite materials using acoustic emission[J]. Composites Part A: Applied Science and Manufacturing, 2015, 75: 77-83. [6] 王宗炼,任会兰,宁建国. 基于小波变换降噪的声发射源定位方法[J]. 振动与冲击,2018,37(04):226-232+248. Wang Zong-lian, Ren Hui-lan, Ning Jian-guo. Acoustic emission source location based on wavelet transform de-noising[J]. Journal of Vibration and Shock, 2018, 37(04): 226-232+248. [7] 柳小勤,汤林江,侯凯泽,等. 基于声发射的滚动轴承损伤定位方法研究[J]. 振动与冲击,2020,39(15):176-182+213. Liu Xiao-qin, Tang Lin-jiang, Hou Kai-ser, et al. Fault localization for rolling bearing based on AE[J]. Journal of Vibration and Shock, 2020,39(15):176-182+213. [8] Zhang F, Pahlavan L, Yang Y. Evaluation of acoustic emission source localization accuracy in concrete structures[J]. Structural Health Monitoring, 2020, 19(6): 2063-2074. [9] Dong L, Zou W, Sun D, et al. Some developments and new insights for microseismic/acoustic emission source localization[J]. Shock and Vibration, 2019, 2019: 15-30. [10] Rentala V K, Mylavarapu P, Gautam J P. Issues in estimating probability of detection of NDT techniques–A model assisted approach[J]. Ultrasonics, 2018, 87: 59-70. [11] Adam C, Fisher J, Michaels J E. Model-assisted probability of detection for ultrasonic structural health monitoring[C]//Proceedings of the 4th European-American Workshop on Reliability of NDE, Berlin, Germany. 2009: 24-26. [12] Ogilvy J A. Model for predicting ultrasonic pulse-echo probability of detection[J]. NDT & E International, 1993, 26(1): 19-29. [13] Subair S M, Balasubramaniam K, Rajagopal P, et al. Finite element simulations to predict probability of detection (PoD) curves for ultrasonic inspection of nuclear components[J]. Procedia Engineering, 2014, 86: 461-468. [14] Guan X, Rasselkorde E M, Abbasi W A, et al. Turbine fatigue reliability and life assessment using ultrasonic inspection: Data acquisition, interpretation, and probabilistic modeling[J]. Quality and Reliability Management and Its Applications, 2016: 415-434. [15] 夏志军,章新华,许林周. 跃变层特性对声纳检测概率的影响[C]//中国声学学会水声学分会.中国声学学会水声学分会2011年全国水声学学术会议论文集.《声学技术》编辑部(Editorial Office of Technical Acoustics),2011 33(3):61-63. XIA Zhi-jun, ZHANG Xin-hua, XU Lin-zhou. The effects of thermocline characteristics on the detection probability of sonar[C]// Proceedings of the 2011 National Conference on Hydroacoustics of the Hydroacoustics Branch of the Chinese Society of Acoustics Editorial Office of Technical Acoustics, 2011, 33(3): 61-63. [16] 高 飞,潘长明,张 韧,等. 温跃层及其变化对被动声纳检测概率影响的研究[J].应用声学,2014,33(02):138-144. GAO Fei, PAN Chang-ming, ZHANG Ren, et al. The effects of thermocline and its variability on the detection probability of passive sonar[J]. Journal of Applied Acoustics, 2014,33(02): 138-144. [17] Guo Y, Ai R, Chen Y, et al. Prediction of Passive Sonar Detection Range in Different Detection Probability[C]//2018 5th International Conference on Systems and Informatics (ICSAI). IEEE, 2018: 1289-1293. [18] 郑全普,张凤伟,霍烁烁.雷达目标检测概率简化算法及应用分析[J].火控雷达技术,2013,42(03):39-42. ZHENG Quan-pu, ZHANG Feng-wei, HUO Shuo-shuo. A simplified algorithm of target detection probability and its application[J]. Fire Control Radar Technology, 2013,42(03): 39-42. [19] 侯道琪,齐 锋,杨 正. 应用于作战仿真的雷达发现概率计算模型[J].电子信息对抗技术,2016,31(01):61-64. HOU Dao-qi, QI Feng, YANG Zheng. Models for Radar Detection Probability Based on Operation Simulation[J]. Electronic Information Warfare Technology, 2016,31(01): 61-64. [20] 吴志林,王 涛,常红伟, 等.搜索雷达发现概率建模与仿真研究[J].火力与指挥控制,2018,43(01):141-144. WU Zhi-lin, WANG Tao, CHANG Hong-wei. Modeling and Simulation of Detection Probability on Search Radar[J]. Fire Control & Command Control, 2018,43(01): 141-144. [21] 苗 铎,杨东凯,许志超,等.GNSS外辐射源雷达低慢小目标探测概率研究[J].北京航空航天大学学报,2023, 49(3):657-664. MIAO Duo, YANG Dong-kai, XU Zhi-chao. LSS Target Detection Probability of Passive Radar Based on GNSS Signals[J]. Journal of Beijing University of Aeronautics and Astronautics, 2023, 49(3):657-664. [22] 韩 晖,肖迎春,白生宝,等.结构疲劳损伤的声发射检测可靠性研究[J].机械设计与制造工程,2014,43(06):76-79. HAN Hui, XIAO Ying-chun, et al., BAI Sheng-bao. Reliability study of acoustic emission detection of structural fatigue damage[J]. Machine Design and Manufacturing Engineering, 2014,43(06): 76. [23] Pollock A. Probability of detection for acoustic emission[J]. Journal of acoustic emission, 2007, 25(1): 231-237. [24] Sause M G R, Linscheid F F, Wiehler M. An experimentally accessible probability of detection model for acoustic emission measurements[J]. Journal of Nondestructive Evaluation, 2018, 37: 1-12. [25] 倪旭,张小柳,卢明辉等.声子晶体和声学超构材料[J].物理,2012,41(10):655-662. NI Xu, ZHANG Xiao-liu, LU Ming-hui, et al.Phononic crystals and acoustic metamaterials[J]. Physics, 2012, 41(10): 655-662. [26] 《ASTM E647-00测量疲劳裂纹扩展速率的标准试验方法》. 《ASTM E647-00 Standard test method for measuring fatigue crack growth rate》.
PDF(2853 KB)

269

Accesses

0

Citation

Detail

Sections
Recommended

/