Structural Monitoring and Maintenance

  • 제7권1호
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  • Pages.13-25
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  • 2020
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  • 2288-6605(pISSN)
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  • 2288-6613(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Analysis of acoustic emission signals during fatigue testing of a M36 bolt using the Hilbert-Huang spectrum

  • Leaman, Felix (Institute for Advanced Mining Technologies, RWTH Aachen University) ;
  • Herz, Aljoscha (Institute for Advanced Mining Technologies, RWTH Aachen University) ;
  • Brinnel, Victoria (Institute for Ferrous Metallurgy, RWTH Aachen University) ;
  • Baltes, Ralph (Institute for Advanced Mining Technologies, RWTH Aachen University) ;
  • Clausen, Elisabeth (Institute for Advanced Mining Technologies, RWTH Aachen University)
  • 투고 : 2019.10.16
  • 심사 : 2020.02.27
  • 발행 : 2020.03.25
⟨ 이전 논문 다음 논문 ⟩

초록

One of the most important aspects in structural health monitoring is the detection of fatigue damage. Structural components such as heavy-duty bolts work under high dynamic loads, and thus are prone to accumulate fatigue damage and cracks may originate. Those heavy-duty bolts are used, for example, in wind power generation and mining equipment. Therefore, the investigation of new and more effective monitoring technologies attracts a great interest. In this study the acoustic emission (AE) technology was employed to detect incipient damage during fatigue testing of a M36 bolt. Initial results showed that the AE signals have a high level of background noise due to how the load is applied by the fatigue testing machine. Thus, an advanced signal processing method in the time-frequency domain, the Hilbert-Huang Spectrum (HHS), was applied to reveal AE components buried in background noise in form of high-frequency peaks that can be associated with damage progression. Accordingly, the main contribution of the present study is providing insights regarding the detection of incipient damage during fatigue testing using AE signals and providing recommendations for further research.

키워드

참고문헌

  1. Aggelis, D.G., Shiotani, T., Papacharalampopoulos, A. and Polyzos, D. (2012), "The influence of propagation path on elastic waves as measured by acoustic emission parameters", Struct. Health Monit., 11(3), 359-366. https://doi.org/10.1177/1475921711419992.
  2. Bhuiyan, M.Y., Bao, J., Poddar, B. and Giurgiutiu, V. (2018), "Toward identifying crack-length-related resonances in acoustic emission waveforms for structural health monitoring applications", Struct. Health Monit., 17(3), 577-585. https://doi.org/10.1177/1475921717707356.
  3. Carlos, M.F. (2003), Heeding the warning sounds from materials, ASTM Standardization News 31, 26-29.
  4. Danyuk, A., Rastegaev, I., Pomponi, E., Linderov, M., Merson, D. and Vinogradov, A. (2017), "Improving of acoustic emission signal detection for fatigue fracture monitoring", Procedia Eng., 176(2017), 284-290. https://doi.org/10.1016/j.proeng.2017.02.323.
  5. Du, F. and Li, D. (2019), "Non-destructive evaluation and pattern recognition for SCRC columns using the AE technique", Struct. Monit. Maint., 6(3), 173-190. https://doi.org/10.12989/smm.2019.6.3.173.
  6. Hamdi, S.E., Le Duff, A., Simon, L., Plantier, G., Sourice, A. and Feuilloy, M. (2013), "Acoustic emission pattern recognition approach based on Hilbert-Huang transform for structural health monitoring in polymer-composite materials", Applied Acoustics 74(5), 746-757. https://doi.org/10.1016/j.apacoust.2012.11.018.
  7. Han, Z., Luo, H., Cao, J. and Wang, H. (2011), "Acoustic emission during fatigue crack propagation in a micro-alloyed steel and welds", Mater. Sci. Eng.: A 528(25-26), 7751-7756. https://doi.org/10.1016/j.msea.2011.06.065.
  8. Hellier, C.J. (2003), Handbook of Nondestructive Evaluation. McGraw-Hill, New York, USA.
  9. Huang, N., Shen, Z., Long, S., Wu, M., Shih, H., Zheng, Q., Yen, N., Tung, C. and Liu, H. (1998), "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis", Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences 454, 903-995. https://doi.org/10.1098/rspa.1998.0193.
  10. Huang, M., Jiang, L., Liaw, P.K., Brooks, C., Seeley, R. and Klarstrom, D. (1998), Using acoustic emission in fatigue and fracture materials research, JOM-e: Research Summary 50.
  11. Keshtgar, A. and Modarres, M. (2013), Acoustic emission-based fatigue crack growth prediction, Proceedings Annual Reliability and Maintainability Symposium, Orlando, USA, January. https://doi.org/10.1109/RAMS.2013.6517715.
  12. Kostryzhev, A., Davis, C.L. and Roberts C. (2013), "Detection of crack growth in rail steel using acoustic emission", Ironmaking & Steelmaking, 40(2), 98-102. https://doi.org/10.1179/1743281212Y.0000000051
  13. Kratochvilova, V., Vlasic, F., Mazal, P. and Palousek, D. (2017), "Fatigue behaviour evaluation of additively and conventionally produced materials by acoustic emission method", Procedia Structural Integrity 5(2017), 393-400. https://doi.org/10.1016/j.prostr.2017.07.187.
  14. Li, L., Peng, Z. and Zhao, M. (2009), "In-situ monitoring of active crack in steel structure using acoustic emission technique", Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation, Zhangjiajie, China, April. https://doi.org/10.1109/ICMTMA.2009.384.
  15. Li, H.N., Yi, T.H., Ren, L., Li, D.S. and Huo, L.S. (2014), "Reviews on innovations and applications in structural health monitoring for infrastructures", Struct. Monit. Maint., 1(1), 1-45. https://doi.org/10.12989/smm.2014.1.1.001.
  16. Lu, C., Ding, P. and Chen, Z. (2011), "Time-frequency analysis of acoustic emission signals generated by tension damage in CFRP", Procedia Eng., 23(2011), 210-215. https://doi.org/10.1016/j.proeng.2011.11.2491.
  17. Marfo, A., Chen, Z. and Li, J. (2013), "Acoustic emission analysis of fatigue crack growth in steel structures", J. Civil Eng. Constr. Technol., 4(7), 239-249. https://doi.org/10.5897/JCECT2013.0224.
  18. Nair, A., Cai, C.S., Pan, F. and Kong, X. (2014), "Acoustic emission monitoring of damage progression in CFRP retrofitted RC beams", Struct. Monit. Maint., 1(1), 111-130. https://doi.org/10.12989/smm.2014.1.1.111.
  19. Ono, K., Cho, H. and Takuma, M. (2005), "The origin of continuous emissions", J. Acoust. Emission, 23(2005), 206-214.
  20. Ruiz-Carcel, C., Hernani-Ros, E., Cao, Y. and Mba, D. (2014), "Use of spectral kurtosis for improving signal to noise ratio of acoustic emission signal from defective bearings", J. Fail. Anal. Prevention, 14(3), 363-371. https://doi.org/10.1007/s11668-014-9805-7.
  21. Siracusano, G., Lamonaca, F., Tomasello, R., Garesci, F., La Corte, A., Carni, D.L., Carpentieri, M., Grimaldi, D. and Finocchio, G. (2015), "A framework for the damage evaluation of acoustic emission signals through Hilbert-Huang transform", Mech. Syst. Signal Pr., 75(2016), 109-122. https://doi.org/10.1016/j.ymssp.2015.12.004
  22. Stranghoner, N., Lorenz, C., Feldmann, M., Citarelli, S., Bleck, W., Munstermann, S. and Brinnel, V. (2018), "Sprodbruchverhalten hochfester Schrauben grosser Abmessungen bei tiefen Temperaturen", Stahlbau 87(1), 17-29. https://doi.org/10.1002/stab.201810559
  23. Vraetz, T., Boos, F.D., Rollinger, D., Bernet, C., Buschgens, C., Baltes, R. and Nienhaus, K. (2016), "Potentials and applications of the acoustic emission technology in mining and heavy machinery", Proceedings of the 11th Aachener Kolloquium fur Instandhaltung, Diagnose und Anlagenuberwachung, Aachen, Germany, November.
  24. WenQin, H., Ying, L., AiJun, G. and Yuan, F.G. (2016), "Damage modes recognition and Hilbert-Huang transform analyses of CFRP laminates utilizing acoustic emission technique", Appl. Compos. Mater., 23(2), 155-178. https://doi.org/10.1007/s10443-015-9454-3.
  25. Yang, Z., Yu, Z., Xie, C. and Huang, Y. (2014), "Application of Hilbert-Huang transform to acoustic emission signal for burn feature extraction in surface grinding process", Measurement, 47(2014), 14-21. http://dx.doi.org/10.1016/j.measurement.2013.08.036.
  26. Yu, Y. and Yang, P. (2009), "Research of acoustic emission characteristics based on the signal parameters", Proceedings of the International Conference on Information Engineering and Computer Science, Wuhan, China, December. https://doi.org/10.1109/ICIECS.2009.5364817
  27. Zhang, X., Wang, Y., Wang, K., Shen, Y. and Hu, H. (2017), "Rail crack detection based on the adaptive noise cancellation method of EMD at high speed", Proceedings of the IEEE International Instrumentation and Measurement Technology Conference, Turin, Italy, May. https://doi.org/10.1109/I2MTC.2017.7969662
  28. Zhao, L., Kang, L. and Yao, S. (2018), "Research and application of acoustic emission signal processing technology", IEEE Access, 7(2018), 984-993. https://doi.org/10.1109/ACCESS.2018.2886095.