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Erschienen in:
2020 | OriginalPaper | Buchkapitel
13. Bayesian Estimation of Acoustic Emission Arrival Times for Source Localization
verfasst von : Ramin Madarshahian, Paul Ziehl, Michael D. Todd
Erschienen in: Model Validation and Uncertainty Quantification, Volume 3
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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.