Abstract
The onset time of an Acoustic Emission (AE) signal is an important feature for source localization. Due to the large volume of data, manually identifying the onset times of AE signals is not possible when AE sensors are used for health monitoring of a structure. Numerous algorithms have been proposed to autonomously obtain the onset time of an AE signal, with differing levels of accuracy. While some methods generally seem to outperform others (even compared to traditional visual inspection of the time signals), this is not true for all signals, even within the same experiment. In this paper, we propose the use of an inverse Bayesian source localization model to develop an autonomous framework to select the most accurate onset time among several competitors. Without loss of generality, three algorithms of Akaike Information Criterion (AIC), Floating Threshold, and Reciprocal-based picker are used to illustrate the capabilities of the proposed method.
Data collected from a concrete specimen are used as an input of the proposed technique. Results show that the proposed technique can select the best onset time candidates from the three mentioned algorithms, automatically. The picked onset time is comparable with manual selection, and accordingly has better accuracy for source localization when compared to any of the single methods.
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Acknowledgements
This work was partially supported by the U.S. Department of Energy under Award Number DE-NE0008544 and also supported by the US Army Engineer Research and Development Center (ERDC) under cooperative agreement W912HZ-17-2-0024. The authors would like to thank Vafa Soltangharaei and Rafal Anay, Ph.D. candidates in the university of South Carolina, for providing technical support for data collection.
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Madarshahian, R., Ziehl, P., Todd, M.D. (2020). Bayesian Estimation of Acoustic Emission Arrival Times for Source Localization. In: Barthorpe, R. (eds) Model Validation and Uncertainty Quantification, Volume 3. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-030-12075-7_13
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DOI: https://doi.org/10.1007/978-3-030-12075-7_13
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