ArticleSensors (Basel, Switzerland)2022
Deep Learning Approaches for Robust Time of Arrival Estimation in Acoustic Emission Monitoring.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A Multi-Indicator Fusion-Based Technique for the Identification of Acoustic Emission Signals During Rock Failure.Sensors (Basel, Switzerland) · 2026Article
- A Deep Learning Model for Detecting the Arrival Time of Weak Underwater Signals in Fluvial Acoustic Tomography Systems.Sensors (Basel, Switzerland) · 2025Article
- Numerical and Experimental Research on Non-Reference Damage Localization Based on the Improved Two-Arrival-Time Difference Method.Sensors (Basel, Switzerland) · 2022Article
- One-Size-Fits-All Policies Are Unacceptable: A Sustainable Management and Decision-Making Model for Schools in the Post-COVID-19 Era.International journal of environmental research and public health · 2022Article
- Machine Learning for Touch Localization on an Ultrasonic Lamb Wave Touchscreen.Sensors (Basel, Switzerland) · 2022Article
- Machine Learning Meets Compressed Sensing in Vibration-Based Monitoring.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
6 authors.
Funding
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Abstract
In this work, different types of artificial neural networks are investigated for the estimation of the time of arrival (ToA) in acoustic emission (AE) signals. In particular, convolutional neural network (CNN) models and a novel capsule neural network are proposed in place of standard statistical strategies which cannot handle, with enough robustness, very noisy scenarios and, thus, cannot be sufficiently reliable when the signal statistics are perturbed by local drifts or outliers. This concept was validated with two experiments: the pure ToA identification capability was firstly assessed on synthetic signals for which a ground truth is available, showing a 10× gain in accuracy when compared to the classical Akaike information criterion (AIC). Then, the same models were tested via experimental data acquired in the framework of a localization problem to identify targets with known coordinates on a square aluminum plate, demonstrating an overreaching precision under significant noise levels.
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