Abstract
Advanced non-destructive monitoring scheme is necessary for modern-day lightweight composite structures used in aerospace industry, due to their susceptibility to barely visible damages from minor impact loads. Acoustic emission (AE) based monitoring of these structures has received significant attention in the past few years primarily due to their possibility of use in operating structures under service loads. However, localization and characterization of damages using AE is still an open area of research. The exploration of the space of signal features collected by a distributed sensor network and its reliable mapping to damage metrics (such as location, nature, intensity) is still far from conclusive. This problem becomes more critical for composite structures with complex features/geometry where the localized effects of discontinuity in geometric or mechanical properties do not make it appropriate to rely on simple signal features (such as time difference of arrival, peak amplitude, etc.) to identify damage. In this work, the AE signal features (which are spatially and temporally correlated) have been mapped to the damage properties empirically with a training dataset using metamodeling techniques. This is used in the online monitoring phase to infer the probabilistic description of the acoustic emission source within a hierarchical Bayesian inference framework. The methodology is tested on a carbon fibre composite panel with stiffeners that is subjected to impact and dynamic fatigue loading. The study presents a generalized machine learning-based automated AE damage detection methodology which both localizes and characterizes damage under varying operational loads.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kundu, A., Adhikari, S., Friswell, M.I.: Stochastic finite elements of discretely parameterized random systems on domains with boundary uncertainty. Int. J. Numer. Methods Eng. 100(3), 183–221 (2014)
Miller, R., Carlos, M., Findlay, R., Godinez-Azcuaga, V., Rhodes, M., Shu, F., Wang, W.: Acoustic emission source location, 3rd edn., pp. 121–146. ASNT (2005)
Eaton, M., Pullin, R., Holford, K.: Towards improved damage location using acoustic emission. J. Mech. Eng. Sci. Proc. Inst. Mech. Eng. Part C 226(9), 2141–2153 (2012)
Kennedy, M.C., O’Hagan, A.: Bayesian calibration of computer models. J. R. Stat. Soc. Ser. B-Stat. Methodol. 63(3), 425–450 (2001)
Kundu, A., Matthies, H., Friswell, M.: Probabilistic optimization of engineering system with prescribed target design in a reduced parameter space. Comput. Methods Appl. Mech. Eng. 337, 281–304 (2018)
Miller, R., Anastasopoulos, A., Carlos, M., Demeski, R., Vallen, H., Walker, J.: Acoustic Emission Signal Processing, 3rd edn., pp. 147–180. ASNT (2005)
Ziola, S., Gorman, M.: Source location in thin plates using cross-correlation, 90, 2551–2556 (1991)
Aljets, D., Chong, A., Wilcox, S., Holford, K.: Acoustic emission source location in plate like structures using a closely arranged triangular sensor array. J. Acoust. Emiss. 28, 85–98 (2010)
Hamstad, M., O’Gallagher, A., Gary, J.: A wavelet transform applied to acoustic emission signals: part 2: source location. J. Acoust. Emiss. 20, 62–82 (2002)
Lokajicek, T., Klima, K.: A first arrival identification system of acoustic emission (AE) signals by means of a higher-order statistics approach. Meas. Sci. Technol. 17, 2461–2466 (2006)
Akaike, H.: Markovian representation of stochastic processes and its application to the analysis of autoregressive moving average processes. Ann. Inst. Stat. Math. 26, 363–387 (1974)
Kurz, J.H., Grosse, C., Reinhardt, H.W.: Strategies for reliable automatic onset time picking of acoustic emission and of ultrasound signals in concrete. Ultrasonics 43, 538–546 (2005)
Pearson, M., Eaton, M., Featherston, C., Pullin, R., Holford, K.: Improved acoustic emission source location during fatigue and impact events in metallic and composite structures, pp. 1–18 (2016)
Kundu, T., Das, S., Jata, K.: Point of impact prediction in isotropic and anisotropic plates from the acoustic emission data. J. Acoust. Soc. Am. 122(4), 2057–2066 (2007)
Kundu, T., Das, S., Martin, S., Jata, K.: Locating point of impact in anisotropic fibre reinforced composite plates. Ultrasonics 48, 193–201 (2008)
Ciampa, F., Meo, M.: A new algorithm for acoustic emission localisation and flexural group velocity determination in anisotropic structures. Compos. Part A 41(12), 1777–1786 (2010)
Baxter, M., Pullin, R., Holford, K., Evans, S.: Delta T source location for acoustic emission. Mech. Syst. Signal Process. 21(3), 1512–1520 (2007)
Eaton, M.J., Pullin, R., Holford, K.: Acoustic emission source location in composite materials using delta T mapping. Compos. Part A 43(6), 856–863 (2012)
Al-Jumaili, S., Pearson, M., Holford, K., Eaton, M., Pullin, R.: Acoustic emission source location in complex structures using full automatic delta T mapping technique 72–73, 513–524 (2016)
Scholey, J., Wilcox, P., Wisnom, M., Friswell, M.: A practical technique for quantifying the performance of acoustic emission systems on plate-like structures. Ultrasonics 49, 538–548 (2009)
Hsu, N.N., Breckenridge, F.R.: Characterization and calibration of acoustic emission sensors. Mater. Eval. 39(1), 60–68 (1981)
ASTM.: A standard guide for determining the reproducibility of acoustic emission sensor response. American Society for Testing and Materials, E976 (2010)
Schumacher, T., Straub, D., Higgins, C.: Toward a probabilistic acoustic emission source location algorithm: a Bayesian approach, 331 (2012)
Zarate, B., Pollock, A., Momeni, S., Ley, O.: Structural health monitoring of liquid-filled tanks: a Bayesian approach for location of acoustic emission sources, 24 (2014)
Kundu, A., Eaton, M.J., Al-Jumali, S., Sikdar, S., Pullin, R.: Acoustic emission based damage localization in composites structures using Bayesian identification. J. Phys.: Conf. Ser. 842 (2017)
Kundu, A., Sikdar, S., Eaton, M., Navaratne, R.: Probabilistic method for damage identification in multi-layered composite structures. In: Proceedings of the 9th European Workshop on Structural Health Monitoring (2018)
Kundu, A., DiazDelaO, F., Adhikari, S., Friswell, M.: A hybrid spectral and metamodeling approach for the stochastic finite element analysis of structural dynamic systems. Comput. Methods Appl. Mech. Eng. 270, 201–219 (2014)
Oakley, J.E., O’Hagan, A.: Probabilistic sensitivity analysis of complex models: a Bayesian approach. J. R. Stat. Soc. B 66(3), 751–769 (2004)
Keane, A., Nair, P.: Computational Approaches for Aerospace Design. Wiley, Chichester (2005)
Sacks, J., Welch, W., Mitchell, T., Wynn, H.: Design and analysis of computer experiments. Stat. Sci. 4(4), 409–435 (1989)
O’Hagan, A.: Bayesian Statistics 4, chap. In: Some Bayesian Numerical Analysis, pp. 345–363. Oxford University Press, Cambridge (1992)
Haylock, R., O’Hagan, A.: Bayesian statistics 5, chap. In: On Inference for Outputs of Computationally Expensive Algorithms with Uncertainty on the Inputs. Oxford University Press, Oxford (1996)
Oakley, J.: Eliciting Gaussian process priors for complex computer codes. Stat. 51(1), 81–97 (2002)
Rougier, J.: Probabilistic inference for future climate using an ensemble of climate model evaluations. Clim. Chang. 81(3), 247–264 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kundu, A., Sikdar, S., Eaton, M., Navaratne, R. (2020). A Generic Framework for Application of Machine Learning in Acoustic Emission-Based Damage Identification. In: Wahab, M. (eds) Proceedings of the 13th International Conference on Damage Assessment of Structures. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-13-8331-1_18
Download citation
DOI: https://doi.org/10.1007/978-981-13-8331-1_18
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-8330-4
Online ISBN: 978-981-13-8331-1
eBook Packages: EngineeringEngineering (R0)