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Sensors and Acoustic Emission Technology for Nondestructive Evaluation

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 3840

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Dr. Anastasios Mpalaskas

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Department of Materials Science and Engineering, University of Ioannina, 451 10 Ioannina, Greece
Interests: acoustic emission; nondestructive evaluation; civil engineering; ultrasound; mechanical testing; restoration of building construction
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Dear Colleagues,

The widespread use of nondestructive techniques for evaluating the structural integrity of building elements has been accepted by most researchers as an accurate and reliable testing procedure. These techniques present many advantages compared to common standard testing methods, and of the monitoring methodologies that assess an entire structure’s volume, Acoustic Emission (AE) is already highly accepted amongst engineers.

AE is a passive nondestructive evaluation (NDE) technique that offers real-time monitoring of defect propagation, which enables characterization of a structure's critical moments in relation to the applied operational load. Moreover, the prediction of a structure's remaining life can also be assisted by characterization of the current cracking mode. The emitted elastic energy possesses waveforms with different characteristics throughout the failure mechanism. These are captured by acoustic emission sensors and their frequency content and waveform parameters are analyzed, thus enabling identification of the fracture mode in building materials using elastic wave methods. Utilizing elastic wave approaches, AE is an innovative methodology for damage investigation in different building materials. Various sensors can be used for monitoring AE while implementing modern technological trends and achievements and focusing on AE technique optimization.

This Special Issue aims to incorporate recent progress and technological achievements in the general field of AE sensors with new advanced methodologies for analyzing AE data (such as machine learning models) to optimize the evaluation of structural integrity in building elements.

Dr. Anastasios Mpalaskas
Guest Editor

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Published Papers (3 papers)

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22 pages, 5106 KiB

Open AccessArticle

Reduction in the Sensor Effect on Acoustic Emission Data to Create a Generalizable Library by Data Merging

by Xi Chen, Nathalie Godin, Aurélien Doitrand and Claudio Fusco

Sensors 2024, 24(8), 2421; https://doi.org/10.3390/s24082421 - 10 Apr 2024

Viewed by 357

Abstract

The aim of this paper is to discuss the effect of the sensor on the acoustic emission (AE) signature and to develop a methodology to reduce the sensor effect. Pencil leads are broken on PMMA plates at different source–sensor distances, and the resulting waves are detected with different sensors. Several transducers, commonly used for acoustic emission measurements, are compared with regard to their ability to reproduce the characteristic shapes of plate waves. Their consequences for AE descriptors are discussed. Their different responses show why similar test specimens and test conditions can yield disparate results. This sensor effect will furthermore make the classification of different AE sources more difficult. In this context, a specific procedure is proposed to reduce the sensor effect and to propose an efficient selection of descriptors for data merging. Principal Component Analysis has demonstrated that using the Z-score normalized descriptor data in conjunction with the Krustal–Wallis test and identifying the outliers can help reduce the sensor effect. This procedure leads to the selection of a common descriptor set with the same distribution for all sensors. These descriptors can be merged to create a library. This result opens up new outlooks for the generalization of acoustic emission signature libraries. This aspect is a key point for the development of a database for machine learning. Full article

(This article belongs to the Special Issue Sensors and Acoustic Emission Technology for Nondestructive Evaluation)

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11 pages, 4051 KiB

Open AccessArticle

Enhancing Structural Health Monitoring with Acoustic Emission Sensors: A Case Study on Composites under Cyclic Loading

by Doyun Jung and Jeonghan Lee

Sensors 2024, 24(2), 371; https://doi.org/10.3390/s24020371 - 08 Jan 2024

Viewed by 696

Abstract

This study conducts an in-depth analysis of the failure behavior of woven GFRP under cyclic loading, leveraging AE sensors for monitoring damage progression. Utilizing destructive testing and AE methods, we observed the GFRP’s response to varied stress conditions. Key findings include identifying distinct failure modes of GFRP and the effectiveness of AE sensors in detecting broadband frequency signals indicative of crack initiation and growth. Notably, the Felicity effect was observed in AE signal patterns, marking a significant characteristic of composite materials. This study introduces the Ib_e-value, based on statistical parameters, to effectively track crack development from inception to growth. The Ib_e-values potential for assessing structural integrity in composite materials is highlighted, with a particular focus on its variation with propagation distance and frequency-dependent attenuation. Our research reveals challenges in measuring different damage modes across frequency ranges and distances. The effectiveness of Ib_e-values, combined with the challenges of propagation distance, underscores the need for further investigation. Future research aims to refine assessment metrics and improve crack evaluation methods in composite materials, contributing to the field’s advancement. Full article

(This article belongs to the Special Issue Sensors and Acoustic Emission Technology for Nondestructive Evaluation)

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16 pages, 6083 KiB

Open AccessArticle

A Comparison of Two Types of Acoustic Emission Sensors for the Characterization of Hydrogen-Induced Cracking

by Dandan Liu, Bin Wang, Han Yang and Stephen Grigg

Sensors 2023, 23(6), 3018; https://doi.org/10.3390/s23063018 - 10 Mar 2023

Cited by 2 | Viewed by 2189

Abstract

Acoustic emission (AE) technology is a non-destructive testing (NDT) technique that is able to monitor the process of hydrogen-induced cracking (HIC). AE uses piezoelectric sensors to convert the elastic waves generated from the growth of HIC into electric signals. Most piezoelectric sensors have resonance and thus are effective for a certain frequency range, and they will fundamentally affect the monitoring results. In this study, two commonly used AE sensors (Nano30 and VS150-RIC) were used for monitoring HIC processes using the electrochemical hydrogen-charging method under laboratory conditions. Obtained signals were analyzed and compared on three aspects, i.e., in signal acquisition, signal discrimination, and source location to demonstrate the influences of the two types of AE sensors. A basic reference for the selection of sensors for HIC monitoring is provided according to different test purposes and monitoring environments. Results show that signal characteristics from different mechanisms can be identified more clearly by Nano30, which is conducive to signal classification. VS150-RIC can identify HIC signals better and provide source locations more accurately. It can also acquire low-energy signals better, which is more suitable for monitoring over a long distance. Full article

(This article belongs to the Special Issue Sensors and Acoustic Emission Technology for Nondestructive Evaluation)

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