Skip to main content
SpringerLink
Log in

Quantification of the Uncertainty of Pattern Recognition Approaches Applied to Acoustic Emission Signals

  • Published:
Journal of Nondestructive Evaluation Aims and scope Submit manuscript

Abstract

Acoustic emission analysis is a nondestructive technique frequently used to assess the integrity of fiber reinforced plastics. Pattern recognition techniques have shown great potential to identify microscopic failure mechanisms in plate-like structures. Because every assignment of an acoustic emission signal to a respective failure mechanism is possibly associated with an error, one key question is the reliability of the assignment method. It is useful to distinguish between the uncertainty of the assignment and the false assignment of an acoustic emission signal to a group of signals. The first is owed to statistical effects and the reliability of the classification method itself. The second is caused by false conclusions or disputable assumptions on the source mechanisms. The present study will focus on the first aspect. For this purpose, we propose a model based algorithm that estimates the uncertainty of a feature based pattern recognition approach based on cluster validity indices. Further, we demonstrate the application of the algorithm to experimental acoustic emission data obtained from a double cantilever beam specimens with unidirectional layup of carbon fiber reinforced polymer. Based on previous investigation we use a pattern recognition approach to distinguish between different failure mechanisms like matrix cracking, interfacial failure and fiber breakage based on the frequency features of the acoustic emission signals. We consider the influence of dispersion and attenuation effects during propagation of Lamb-waves on the extracted acoustic emission features. This is done by investigating the influence of source-sensor distance by test sources like pencil lead breaks and piezoelectric pulsers. Using the model based algorithm it is possible to calculate the uncertainty of the pattern recognition results as a function of source-sensor distance. It is found that dispersion effects of Lamb-waves do not seriously affect the distinction between microscopic failure mechanisms for source-sensor distances up to 375 mm. We demonstrate that the spatial distribution of acoustic emission sources has a larger impact on the uncertainty of assignment than the absolute source-sensor distance. Applying the proposed algorithm to the current experimental setup, we obtain an uncertainty of classification below 7 % for source-sensor distances below 375 mm. Attenuation is quantified to be 0.165 dB/mm for the A 0-mode and 0.047 dB/mm for the S 0-mode. Within the source-sensor distance of 375 mm this causes severe attenuation of the signal amplitude and thus prohibits detection of weak acoustic emission signals long before the uncertainty of the classification method reaches 10 %.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Abbreviations

CFRP:

Carbon fiber reinforced plastics

AE:

Acoustic emission

S i :

Lamb-wave mode, symmetric, i-th order

A i :

Lamb-wave mode, antisymmetric, i-th order

SH i :

Shear-horizontal wave mode, i-th order

C ij :

Elastic coefficients

DB :

Davies-Bouldin Index

TOU :

Tou Index

S :

Rousseuw’s silhouette value

γ :

Hubert’s Gamma statistics

DCB:

Double Cantilever Beam

J :

Degree of cluster separation

RAND :

Rand Index

C :

Cluster validity measure

C 0 :

Fit parameter (logistics function)

s :

Fit parameter (logistics function)

h :

Fit parameter (logistics function)

A min :

Fit parameter (logistics function)

A max :

Fit parameter (logistics function)

A :

Range of univariate distribution

N :

Univariate distribution

References

  1. Große, C., Ohtsu, M.: Acoustic Emission Testing. Springer, Berlin (2008)

    Book  Google Scholar 

  2. Li, J., Qi, G.: Improving source location accuracy of acoustic emission in complicated structures. J. Nondestruct. Eval. 28, 1–8 (2009). doi:10.1007/s10921-009-0042-z

    Article  Google Scholar 

  3. Wang, L., Yuan, F.G.: Group velocity and characteristic wave curves of Lamb waves in composites: modeling and experiments. Compos. Sci. Technol. 67, 1370–1384 (2007). doi:10.1016/j.compscitech.2006.09.023

    Article  Google Scholar 

  4. Huguet, S., Godin, N., Gaertner, R., Salmon, L., Villard, D.: Use of acoustic emission to identify damage modes in glass fibre reinforced polyester. Compos. Sci. Technol. 62, 1433–1444 (2002). doi:10.1016/S0266-3538(02)00087-8

    Article  Google Scholar 

  5. Ramirez-Jimenez, C.R., Papadakis, N., Reynolds, N., Gan, T., Purnell, P., Pharaoh, M.: Identification of failure modes in glass/polypropylene composites by means of the primary frequency content of the acoustic emission event. Compos. Sci. Technol. 64, 1819–1827 (2004). doi:10.1016/j.compscitech.2004.01.008

    Article  Google Scholar 

  6. Marec, A., Thomas, J.H., Guerjouma, R.: Damage characterization of polymer-based composite materials: multivariable analysis and wavelet transform for clustering acoustic emission data. Mech. Syst. Signal Process. 22, 1441–1464 (2008). doi:10.1016/j.ymssp.2007.11.029

    Article  Google Scholar 

  7. Li, X., Ramirez, C., Hines, E.L., Leeson, M.S., Purnell, P., Pharaoh, M.: Pattern recognition of fiber-reinforced plastic failure mechanism using computational intelligence techniques. In: Neural Networks, IEEE World Congress on Computational Intelligence, pp. 2340–2345 (2008)

    Google Scholar 

  8. Philippidis, T., Nikolaidis, V., Anastassopoulos, A.: Damage characterisation of C/C laminates using neural network techniques on AE signals. Nondestruct. Test. Eval. Int. 31, 329–340 (1998). doi:10.1016/S0963-8695(98)00015-2

    Google Scholar 

  9. Ativitavas, N., Pothisiri, T., Fowler, T.J.: Identification of fiber-reinforced plastic failure mechanisms from acoustic emission data using neural networks. J. Compos. Mater. 40, 193–226 (2006). doi:10.1177/0021998305053458

    Article  Google Scholar 

  10. Kostopoulos, V., Karapappas, P., Loutas, T., Vavouliotis, A., Paipetis, A., Tsotra, P.: Interlaminar fracture toughness of carbon fibre-reinforced polymer laminates with nano- and micro-fillers. Strain 47, 269–282 (2011). doi:10.1111/j.1475-1305.2008.00612.x

    Article  Google Scholar 

  11. Sause, M.G.R., Gribov, A., Unwin, A.R., Horn, S.: Pattern recognition approach to identify natural clusters of acoustic emission signals. Pattern Recognit. Lett. 33, 17–23 (2012). doi:10.1016/j.patrec.2011.09.018

    Article  Google Scholar 

  12. Sause, M.G.R., Müller, T., Horoschenkoff, A., Horn, S.: Quantification of failure mechanisms in mode-I loading of fiber reinforced plastics utilizing acoustic emission analysis. Compos. Sci. Technol. 72, 167–174 (2012). doi:10.1016/j.compscitech.2011.10.013

    Article  Google Scholar 

  13. Polikar, R.: Pattern recognition. In: Wiley Encyclopedia of Biomedical Engineering. Wiley, New York (2006)

    Google Scholar 

  14. Sause, M.G.R., Horn, S.: Influence of specimen geometry on acoustic emission signals in fiber reinforced composites: FEM-simulations and experiments. In: Conf. Proc. 29th European Conf. on Acoustic Emission Testing, Vienna, Austria (2010)

    Google Scholar 

  15. Sause, M.G.R.: Identification of Failure Mechanisms in Hybrid Materials Utilizing Pattern Recognition Techniques Applied to Acoustic Emission Signals (2010)

    Google Scholar 

  16. Sause, M.G.R., Horn, S.: Simulation of acoustic emission in planar carbon fiber reinforced plastic specimens. J. Nondestruct. Eval. 29, 123–142 (2010). doi:10.1007/s10921-010-0071-7

    Article  Google Scholar 

  17. Anastassopoulos, A.A., Philippidis, T.P.: Clustering methodology for the evaluation of acoustic emission from composites. J. Acoust. Emiss. 13, 11–21 (1995)

    Google Scholar 

  18. Günter, S., Bunke, H.: Validation indices for graph clustering. Pattern Recognit. Lett. 24, 1107–1113 (2003). doi:10.1016/S0167-8655(02)00257-X

    Article  MATH  Google Scholar 

  19. Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 1, 224–227 (1979)

    Article  Google Scholar 

  20. Tou, J.T.: DYNOC—a dynamic optimal cluster-seeking technique. Int. J. Comput. Inf. Sci. 8, 541–547 (1979)

    Article  MathSciNet  MATH  Google Scholar 

  21. Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)

    Article  MATH  Google Scholar 

  22. Hubert, L.J., Arabie, P.: Comparing partitions. J. Classif. 2, 193–218 (1985)

    Article  Google Scholar 

  23. Milligan, G.W.: An algorithm for generating artificial test clusters. Psychometrika 50, 123–127 (1985)

    Article  Google Scholar 

  24. Qiu, W.L., Joe, H.: Generation of random clusters with specified degree of separation. J. Classif. 23, 315–334 (2006). doi:10.1007/s00357-006-0018-y

    Article  MathSciNet  Google Scholar 

  25. Qiu, W.L., Joe, H.: clusterGeneration: random cluster generation (with specified degree of separation) (2009)

  26. Qiu, W.L., Joe, H.: Separation index and partial membership for clustering. Comput. Stat. Data Anal. 50, 585–603 (2006). doi:10.1016/j.csda.2004.09.009

    Article  MathSciNet  MATH  Google Scholar 

  27. Rand, W.M.: Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 336, 846–850 (1971). doi:10.2307/2284239

    Article  Google Scholar 

  28. Choi, H.-I., Williams, J.W.: Improved time-frequency representation of multicomponent signals using exponential kernels. IEEE Trans. Acoust. Speech Signal Process. 37, 862–871 (1989). doi:10.1109/ASSP.1989.28057

    Article  Google Scholar 

  29. Skopalik, W., Sause, M.G.R.: DensityVille manual rev. 0.99. http://www.physik.uni-augsburg.de/exp2/downloads/ (2012). Accessed 01 August 2012

  30. Pollock, A.A.: Critical AE problems for the researcher. J. Acoust. Emiss. 9, 140–141 (1990)

    Google Scholar 

  31. Downs, K.S., Hamstad, M.A.: Correlation of regions of acoustic emission activity with burst locations for spherical graphite/epoxy pressure vessels. J. Acoust. Emiss. 13, 56–66 (1995)

    Google Scholar 

Download references

Acknowledgements

We would like to thank Marvin A. Hamstad for providing the dispersion curve results used in this publication.

Author information

Authors and Affiliations

  1. Institute for Physics, Experimental Physics II, University of Augsburg, 86135, Augsburg, Germany

    Markus G. R. Sause & Siegfried Horn

Authors
  1. Markus G. R. Sause
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Siegfried Horn
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Markus G. R. Sause.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sause, M.G.R., Horn, S. Quantification of the Uncertainty of Pattern Recognition Approaches Applied to Acoustic Emission Signals. J Nondestruct Eval 32, 242–255 (2013). https://doi.org/10.1007/s10921-013-0177-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10921-013-0177-9

Keywords

Search

Navigation