Author
X. Yang
Bio: X. Yang is an academic researcher from Sichuan University. The author has contributed to research in topics: Acoustic emission & Acoustic source localization. The author has an hindex of 1, co-authored 1 publications receiving 40 citations.
Papers
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TL;DR: A two-step hybrid technique is proposed in this paper for predicting acoustic source in anisotropic plates that always reduced the prediction error irrespective of whether the final prediction coincided with the actual source location or not.
55 citations
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TL;DR: In this article, a new and improved fully automatic delta T mapping technique is presented, where a clustering algorithm is used to automatically identify and select the highly correlated events at each grid point whilst the minimum difference approach is employed to determine the source location.
108 citations
01 May 2018
TL;DR: Two deep learning approaches to localize acoustic emissions (AE) sources within metallic plates with geometric features, such as rivet-connected stiffeners and a stack of autoencoders and a convolutional neural network are introduced.
Abstract: This paper introduces two deep learning approaches to localize acoustic emissions (AE) sources within metallic plates with geometric features, such as rivet-connected stiffeners. In particular, a stack of autoencoders and a convolutional neural network are used. The idea is to leverage the reflection and reverberation patterns of AE waveforms as well as their dispersive and multimodal characteristics to localize their sources with only one sensor. Specifically, this paper divides the structure into multiple zones and finds the zone in which each source occurs. To train, validate, and test the deep learning networks, fatigue cracks were experimentally simulated by Hsu–Nielsen pencil lead break tests. The pencil lead breaks were carried out on the surface and at the edges of the plate. The results show that both deep learning networks can learn to map AE signals to their sources. These results demonstrate that the reverberation patterns of AE sources contain pertinent information to the location of their sources.
102 citations
TL;DR: A new acoustic emission (AE) source localization for isotropic plates with reflecting boundaries that has no blind spot leverages multimodal edge reflections to identify AE sources with only a single sensor is presented.
97 citations
TL;DR: A new technique is introduced for acoustic source localization in an anisotropic plate by dealing with non‐circular shape of wave fronts, which means the acoustic source could be successfully localized without knowing the material properties of the plate.
53 citations
TL;DR: This study considers different shapes of the wave front generated during an acoustic event and develops a methodology to localize the acoustic source in an anisotropic plate from those wave front shapes.
51 citations