publication . Article . Other literature type . Conference object . 2016

Acoustic emission source location in complex structures using full automatic delta T mapping technique

Matthew R. Pearson; Karen Margaret Holford; Rhys Pullin; Safaa Kh. Al-Jumaili; Safaa Kh. Al-Jumaili; Mark Jonathan Eaton;
Open Access English
  • Published: 01 May 2016
  • Publisher: Elsevier
  • Country: United Kingdom
Abstract
An easy to use, fast to apply, cost-effective, and very accurate non-destructive testing (NDT) technique for damage localisation in complex structures is key for the uptake of structural health monitoring systems (SHM). Acoustic emission (AE) is a viable technique that can be used for SHM and one of the most attractive features is the ability to locate AE sources. The time of arrival (TOA) technique is traditionally used to locate AE sources, and relies on the assumption of constant wave speed within the material and uninterrupted propagation path between the source and the sensor. In complex structural geometries and complex materials such as composites, this a...
Subjects
free text keywords: TA, Control and Systems Engineering, Signal Processing, Mechanical Engineering, Civil and Structural Engineering, Aerospace Engineering, Computer Science Applications, Cluster analysis, Time of arrival, Structural health monitoring, Nondestructive testing, business.industry, business, Acoustic emission, Machine learning, computer.software_genre, computer, Operator (computer programming), Grid, Artificial intelligence, Pattern recognition, Robustness (computer science), Engineering
Related Organizations
32 references, page 1 of 3

[1] K.R. Miller, E.K. Hill, Non-Destructive Testing Handbook, Acoustic Emission Testing, American Society for Non-Destructive Testing, 2005.

[2] T. Kundu, Acoustic source localization, Ultrasonics 54 (2014) 25-38.

[3] R. Pullin, M. Baxter, M.J. Eaton, K.M. Holford, S.L. Evans, Novel acoustic emission source detection, J. Acoust. Emiss. 25 (2007) 215-223. ISSN 0730- 0050.

[4] J. Hensman, R. Mills, S.G. Pierce, K. Worden, M. Eaton, Locating acoustic emission sources in complex structures using gaussian processes, Mech. Syst. Signal Process. 24 (2010) 211-223.

[5] P.S. Earle, P.M. Shearer, Characterization of global seismograms using an automatic-picking algorithm, Bull. Seism. Soc. Am. 84 (1994) 366-376.

[6] S.M. Ziola, M.R. Gorman, Source location in thin plates using cross‐correlation, J. Acoust. Soc. Am. 90 (1991) 2551-2556. [OpenAIRE]

[7] M. Hamstad, A. O'Gallagher, J. Gary, A wavelet transform applied to acoustic emission, J. Acoust. Emiss. 20 (2002) 39-61.

[8] T. Lokajíček, K. Klima, A first arrival identification system of acoustic emission (AE) signals by means of a high-order statistics approach, Meas. Sci. Technol. 17 (2006) 2461. [OpenAIRE]

[9] J. Wang, T.-L. Teng, Artificial neural network-based seismic detector, Bull. Seism. Soc. Am. 85 (1995) 308-319.

[10] H. Akaike, Markovian representation of stochastic processes and its application to the analysis of autoregressive moving average processes, Ann. Inst. Stat. Math. 26 (1974) 363-387. [OpenAIRE]

[11] N. Maeda, A method for reading and checking phase times in auto-processing system of seismic wave data, Zisin ¼Jishin 38 (1985) 365-379.

[12] J.H. Kurz, C.U. Grosse, H.-W. Reinhardt, Strategies for reliable automatic onset time picking of acoustic emissions and of ultrasound signals in concrete, Ultrasonics 43 (2005) 538-546.

[13] P. Sedlak, Y. Hirose, S.A. Khan, M. Enoki, J. Sikula, New automatic localization technique of acoustic emission signals in thin metal plates, Ultrasonics 49 (2009) 254-262.

[14] R. Sleeman, T. van Eck, Robust automatic P-phase picking: an on-line implementation in the analysis of broadband seismogram recordings, Phys. Earth Planet. Inter. 113 (1999) 265-275.

[15] T. He, Q. Pan, Y. Liu, X. Liu, D. Hu, Near-field beamforming analysis for acoustic emission source localization, Ultrasonics 52 (2012) 587-592.

32 references, page 1 of 3
Abstract
An easy to use, fast to apply, cost-effective, and very accurate non-destructive testing (NDT) technique for damage localisation in complex structures is key for the uptake of structural health monitoring systems (SHM). Acoustic emission (AE) is a viable technique that can be used for SHM and one of the most attractive features is the ability to locate AE sources. The time of arrival (TOA) technique is traditionally used to locate AE sources, and relies on the assumption of constant wave speed within the material and uninterrupted propagation path between the source and the sensor. In complex structural geometries and complex materials such as composites, this a...
Subjects
free text keywords: TA, Control and Systems Engineering, Signal Processing, Mechanical Engineering, Civil and Structural Engineering, Aerospace Engineering, Computer Science Applications, Cluster analysis, Time of arrival, Structural health monitoring, Nondestructive testing, business.industry, business, Acoustic emission, Machine learning, computer.software_genre, computer, Operator (computer programming), Grid, Artificial intelligence, Pattern recognition, Robustness (computer science), Engineering
Related Organizations
32 references, page 1 of 3

[1] K.R. Miller, E.K. Hill, Non-Destructive Testing Handbook, Acoustic Emission Testing, American Society for Non-Destructive Testing, 2005.

[2] T. Kundu, Acoustic source localization, Ultrasonics 54 (2014) 25-38.

[3] R. Pullin, M. Baxter, M.J. Eaton, K.M. Holford, S.L. Evans, Novel acoustic emission source detection, J. Acoust. Emiss. 25 (2007) 215-223. ISSN 0730- 0050.

[4] J. Hensman, R. Mills, S.G. Pierce, K. Worden, M. Eaton, Locating acoustic emission sources in complex structures using gaussian processes, Mech. Syst. Signal Process. 24 (2010) 211-223.

[5] P.S. Earle, P.M. Shearer, Characterization of global seismograms using an automatic-picking algorithm, Bull. Seism. Soc. Am. 84 (1994) 366-376.

[6] S.M. Ziola, M.R. Gorman, Source location in thin plates using cross‐correlation, J. Acoust. Soc. Am. 90 (1991) 2551-2556. [OpenAIRE]

[7] M. Hamstad, A. O'Gallagher, J. Gary, A wavelet transform applied to acoustic emission, J. Acoust. Emiss. 20 (2002) 39-61.

[8] T. Lokajíček, K. Klima, A first arrival identification system of acoustic emission (AE) signals by means of a high-order statistics approach, Meas. Sci. Technol. 17 (2006) 2461. [OpenAIRE]

[9] J. Wang, T.-L. Teng, Artificial neural network-based seismic detector, Bull. Seism. Soc. Am. 85 (1995) 308-319.

[10] H. Akaike, Markovian representation of stochastic processes and its application to the analysis of autoregressive moving average processes, Ann. Inst. Stat. Math. 26 (1974) 363-387. [OpenAIRE]

[11] N. Maeda, A method for reading and checking phase times in auto-processing system of seismic wave data, Zisin ¼Jishin 38 (1985) 365-379.

[12] J.H. Kurz, C.U. Grosse, H.-W. Reinhardt, Strategies for reliable automatic onset time picking of acoustic emissions and of ultrasound signals in concrete, Ultrasonics 43 (2005) 538-546.

[13] P. Sedlak, Y. Hirose, S.A. Khan, M. Enoki, J. Sikula, New automatic localization technique of acoustic emission signals in thin metal plates, Ultrasonics 49 (2009) 254-262.

[14] R. Sleeman, T. van Eck, Robust automatic P-phase picking: an on-line implementation in the analysis of broadband seismogram recordings, Phys. Earth Planet. Inter. 113 (1999) 265-275.

[15] T. He, Q. Pan, Y. Liu, X. Liu, D. Hu, Near-field beamforming analysis for acoustic emission source localization, Ultrasonics 52 (2012) 587-592.

32 references, page 1 of 3
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