Features of Planar Localization of Acoustic Emission Sources via the Inglada’s Triangulation Algorithm

封面

如何引用文章

全文:

开放存取 开放存取
受限制的访问 ##reader.subscriptionAccessGranted##
受限制的访问 订阅存取

详细

This paper presents a methodology for enhancing the efficiency of acoustic emission (AE) source detection during planar localization using the Inglada’s algorithm. The study analyzes the main factors affecting the accuracy of AE source localization when using a standard planar localization approach. These factors include the threshold-based method of determining the signal registration time by AE sensors, which is based on detecting the moment when the rising wavefront voltage exceeds the discrimination threshold (uth), the signal sampling frequency (fd), and the influence of the medium’s dispersion properties on the attenuation of signal amplitude and wave propagation speed. To reduce the impact of these factors on the localization accuracy of AE sources, a novel methodology is proposed based on the use of correlation dependencies of AE pulse propagation speed on the amplitude of the recorded signals, as well as on accounting for the delay in the registration time of AE pulses during threshold detection. A series of preliminary experiments was conducted to implement the proposed methodology, where AE pulses were generated using an electronic simulator with a maximum amplitude level of um = 45—90 dB. The position of the AE pulse source varied in the range of 150 to 700 mm relative to the receiving sensors of the antenna array. As a result of applying the developed methodology, the probability of AE source detection increased to p = 0,71, compared to p = 0,36 when using the standard approach.

全文:

受限制的访问

作者简介

Yu. Matvienko

Blagonravov Institute of Mechanical Engineering Research of the Russian Academy of Sciences

编辑信件的主要联系方式.
Email: chernovdv@inbox.ru
俄罗斯联邦, Moscow

I. Vasiliev

Blagonravov Institute of Mechanical Engineering Research of the Russian Academy of Sciences

Email: chernovdv@inbox.ru
俄罗斯联邦, Moscow

T. Balandin

Blagonravov Institute of Mechanical Engineering Research of the Russian Academy of Sciences

Email: chernovdv@inbox.ru
俄罗斯联邦, Moscow

D. Chernov

Blagonravov Institute of Mechanical Engineering Research of the Russian Academy of Sciences

Email: chernovdv@inbox.ru
俄罗斯联邦, Moscow

参考

  1. Ivanov V.I., Barat V.A. Akustiko-emissionnaya diagnostika (Acoustic-Emission Diagnostics). Moscow: Spektr, 2017.
  2. Bigus G.A., Daniev Yu.F., Bystrova N.A., Galkin D.I. Osnovy diagnostiki tekhnicheskikh ustroistv i sooruzhenii (Fundamentals of Diagnostics of Technical Devices and Structures). Moscow: Mosk. Gos. Tekh. Univ. im. N.E. Baumana, 2015.
  3. Matvienko Yu.G., Vasil’ev I.E., Chernov D.V., Ivanov V.I., Elizarov S.V. Problemy lokatsii istochnikov akusticheskoy emissii (Problems of Locating Acoustic Emission Sources) // Defectoscopiya. 2021. No. 9. P. 35—44. doi: 10.29296/defectoscopy.2021.9.35-44
  4. Ser’eznov A.N., Stepanova L.N., Kabanov S.I. Akustiko-emissionnyy kontrol’ defektov svarki (Acoustic-Emission Control of Welding Defects). Novosibirsk: Nauka, 2018.
  5. Matvienko Yu.G., Ivanov V.I., Vasil’ev I.E., Chernov D.V., Mishchenko I.V. Opredelenie skorosti rasprostraneniya volnovogo paketa v kompozitnykh materialakh (Determination of Wave Packet Propagation Velocity in Composite Materials) // Pribory i Tekhnika Eksperimenta. 2020. No. 1. P. 115—120. doi: 10.31857/S0032816220010231
  6. Wotzka D. Influence of Frequency and Distance on Acoustic Emission Velocity Propagating in Various Dielectrics // Applied Sciences (Switzerland). 2020. V. 10. No. 9. P. 3305. doi: 10.3390/app10093305
  7. Chen S., Yang C., Wang G., Liu W. Similarity assessment of acoustic emission signals and its application in source localization // Ultrasonics. 2017. V. 75. P. 36—45. doi: 10.1016/j.ultras.2016.11.005
  8. Marchenkov A., Zhgut D., Moskovskaya D., Kulikova E., Vasiliev I., Chernov D., Mishchenko I. Estimation of acoustic source positioning error determined by one-dimensional linear location technique // Applied Sciences (Switzerland). 2022. V. 12. No. 1. doi: 10.3390/app12010224
  9. Kalafat S., Sause M.G.R. Acoustic emission source localization by artificial neural networks // Structural Health Monitoring. 2015. V. 14. No. 6. P. 633—647. doi: 10.1177/1475921715607408
  10. Matvienko Yu.G., Vasil’ev I.E., Chernov D.V., Kozhevnikov A.V., Mishchenko I.V. Povishenie veroyatnosti viavleniya istochnikov akusticheskoy emissii s pomoshch’yu iskusstvennykh neyronnykh setey (Increasing the Probability of Detecting Acoustic Emission Sources Using Artificial Neural Networks) // Defectoscopiya. 2022. No. 5. P. 3—12. doi: 10.29296/defectoscopy.2022.5.3-12
  11. Grabowski K., Gawronski M., Staszewski W.J., Uhl T., Packo P. Acoustic emission localization through excitability prediction and dispersion removal technique / Progress in Acoustic Emission XVIII, JSNDI & IIIAE-23 (December 5—9, 2016). P. 217—220.
  12. Yang X., Zhou J., Gao C., Zhang P., Liu T., Zhang K., Zhang C. An acoustic emission source localization approach based on time-reversal technology for additive manufacturing / MATEC Web of Conferences. 2022. V. 355. No. 5. P. 01008. doi: 10.1051/matecconf/202235501008
  13. Al-Jumaili S.K., Pearson M.R., Holford K.M., Eaton M.J., Pullin R. Acoustic emission source location in complex structures using full automatic delta T mapping technique // Mechanical Systems and Signal Processing. 2016. V. 72—73. P. 513—524. doi: 10.1016/j.ymssp.2016.09.005
  14. Middleton C.A., McCrory J.P., Greene R.J., Holford K., Patterson E.A. Detecting and Monitoring Cracks in Aerospace Materials Using Post-Processing of TSA and AE Data // Metals. 2019. V. 9. No. 7. P. 748. doi: 10.3390/met9070748
  15. Spencer S.J. The two-dimensional source location problem for time differences of arrival at minimal element monitoring arrays // The Journal of the Acoustical Society of America. 2007. V. 121. No. 6. P. 3579—3594. doi: 10.1121/1.2717430
  16. Ser’eznov A.N., Stepanova L.N., Murav’ev V.V., Komarov K.L., Kabanov S.I., Lebedev E.Yu., Kojemakin V.L., Pan’kov A.F. Akustiko-emissionnaya diagnostika konstruktsiy (Acoustic-Emission Diagnostics of Structures). Moscow: Radio i Svyaz, 2000. P. 92—112.

补充文件

附件文件
动作
1. JATS XML
下载
2. Fig. 1. Determination of AE source location using the Inglada algorithm: Ri = Vg(ti - t), where ti - time of pulse registration by the i-th SAE; Xi, Yi - coordinates of the SAE installation (■); X, Y - coordinates of the AE source (X)

下载 (111KB)
索引源数据
3. Fig. 2. Results of planar location of AE sources using the standard Inglada algorithm

下载 (172KB)
索引源数据
4. Fig. 3. Characteristic shapes of AE pulses recorded with PAE No. 2 (a); PAE No. 3 (b); PAE No. 1 (c). Coordinate of AE signals simulation (X; Y) = (225; 490) mm

下载 (586KB)
索引源数据
5. Fig. 4. Dependence of the normalised velocity (Vg/c) of AE pulse propagation on the level of their amplitude (um)

下载 (200KB)
索引源数据
6. Fig. 5. Accuracy of determination of AE pulse registration time

下载 (148KB)
索引源数据
7. Fig. 6. Results of planar AE source location using the standard (a) and developed (b) algorithms: ■ - position of PAE; X - position of AE sources; ● - indications of AE sources in localisation clusters with radius R = 41 mm; ● - indications of AE sources located outside the localisation clusters

下载 (430KB)
索引源数据
8. Fig. 7. Histogram of error distribution (p) of AE-event indications obtained using the standard (a) and developed (b) methods

下载 (245KB)
索引源数据
9. Fig. 8. Dependence of the probability of detection of AE sources as a function of the radius of the localisation cluster calculated by the results of application of the standard (1) and proposed (2) algorithms

下载 (165KB)
索引源数据

版权所有 © Russian Academy of Sciences, 2024