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Acoustic Emission Sensors for Structural Health Monitoring

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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: closed (31 August 2021) | Viewed by 20070

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Special Issue Editor

Prof. Dr. Tomoki Shiotani

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Guest Editor

iTi Laboratory, Department of Civil & Earth Resources Engineerg, Kyoto University, Kyoto 615-8540, Japan
Interests: civil engineering materials; assessment of deterioration; NDT; sensors; AE; UT; FOS; tomography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The damage assessment of ageing structures is crucial worldwide in order to ensure the present safety and establish the future life of the structures. Routine inspections (e.g., a 5-year span) and continuous inspections—namely, real-time monitoring—are typically required monitoring tasks for the execution of preventive maintenance and alerting eventual failure, in turn. Acoustic emission (AE) sensors will contribute to these. In routine inspection, secondary AE activity induced by existing defects when applying dynamic load in service is utilized to visualize the momentary condition of internal damage of the structure. Moreover, in continuous monitoring, primary AE activity generated due to crack occurrence can indicate the approach of impending failure. The above facts have been well known to AE engineers and researchers for some time; however, it has been difficult to realize the official practical inspection. Requirements such as power supply, data transmission, readiness to connect sensors or systems, and sophisticated data interpretation were not well-developed. The rapid development of relevant IoT technologies to AE measurements solves these issues (e.g., fast signal acquisition and transmission, edge computing, wireless/UAV technologies, energy harvesting, big data analysis, etc.). Thanks to the innovative progress of AE sensors and related technologies, in this Special Issue, we welcome the contribution of state-of-the-art AE applications and relevant technologies to AE sensors for structural health monitoring, exhibiting potential applications of the AE technology to promote the mutual exchange of successful findings among a variety of fields’ engineers and researchers.

Prof. Dr. Tomoki Shiotani
Guest Editor

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Keywords

ageing structures
accelerometers
AE sensors
structural health assessment
preventive maintenance
failure alert
recent relevant technologies to AE testing

Published Papers (6 papers)

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Research

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19 pages, 6713 KiB

Open AccessArticle

Acoustic Emission Monitoring of Carbon Fibre Reinforced Composites with Embedded Sensors for In-Situ Damage Identification

by Arnaud Huijer, Christos Kassapoglou and Lotfollah Pahlavan

Sensors 2021, 21(20), 6926; https://doi.org/10.3390/s21206926 - 19 Oct 2021

Cited by 21 | Viewed by 3147

Abstract

Piezoelectric sensors can be embedded in carbon fibre-reinforced plastics (CFRP) for continuous measurement of acoustic emissions (AE) without the sensor being exposed or disrupting hydro- or aerodynamics. Insights into the sensitivity of the embedded sensor are essential for accurate identification of AE sources. Embedded sensors are considered to evoke additional modes of degradation into the composite laminate, accompanied by additional AE. Hence, to monitor CFRPs with embedded sensors, identification of this type of AE is of interest. This study (i) assesses experimentally the performance of embedded sensors for AE measurements, and (ii) investigates AE that emanates from embedded sensor-related degradation. CFRP specimens have been manufactured with and without embedded sensors and tested under four-point bending. AE signals have been recorded by the embedded sensor and two reference surface-bonded sensors. Sensitivity of the embedded sensor has been assessed by comparing centroid frequencies of AE measured using two sizes of embedded sensors. For identification of embedded sensor-induced AE, a hierarchical clustering approach has been implemented based on waveform similarity. It has been confirmed that both types of embedded sensors (7 mm and 20 mm diameter) can measure AE during specimen degradation and final failure. The 7 mm sensor showed higher sensitivity in the 350–450 kHz frequency range. The 20 mm sensor and the reference surface-bounded sensors predominately featured high sensitivity in ranges of 200–300 kHz and 150–350 kHz, respectively. The clustering procedure revealed a type of AE that seems unique to the region of the embedded sensor when under combined in-plane tension and out-of-plane shear stress. Full article

(This article belongs to the Special Issue Acoustic Emission Sensors for Structural Health Monitoring)

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16 pages, 7299 KiB

Open AccessArticle

Crack Length Measurement Using Convolutional Neural Networks and Image Processing

by Yingtao Yuan, Zhendong Ge, Xin Su, Xiang Guo, Tao Suo, Yan Liu and Qifeng Yu

Sensors 2021, 21(17), 5894; https://doi.org/10.3390/s21175894 - 01 Sep 2021

Cited by 21 | Viewed by 4255

Abstract

Fatigue failure is a significant problem in the structural safety of engineering structures. Human inspection is the most widely used approach for fatigue failure detection, which is time consuming and subjective. Traditional vision-based methods are insufficient in distinguishing cracks from noises and detecting crack tips. In this paper, a new framework based on convolutional neural networks (CNN) and digital image processing is proposed to monitor crack propagation length. Convolutional neural networks were first applied to robustly detect the location of cracks with the interference of scratch and edges. Then, a crack tip-detection algorithm was established to accurately locate the crack tip and was used to calculate the length of the crack. The effectiveness and precision of the proposed approach were validated through conducting fatigue experiments. The results demonstrated that the proposed approach could robustly identify a fatigue crack surrounded by crack-like noises and locate the crack tip accurately. Furthermore, crack length could be measured with submillimeter accuracy. Full article

(This article belongs to the Special Issue Acoustic Emission Sensors for Structural Health Monitoring)

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18 pages, 9238 KiB

Open AccessCommunication

A New Fracture Detection Algorithm of Low Amplitude Acoustic Emission Signal Based on Kalman Filter-Ripple Voltage

by Seong-Min Jeong, Seokmoo Hong and Jong-Seok Oh

Sensors 2021, 21(12), 4247; https://doi.org/10.3390/s21124247 - 21 Jun 2021

Cited by 2 | Viewed by 2274

Abstract

In this study, an acoustic emission (AE) sensor was utilized to predict fractures that occur in a product during the sheet metal forming process. An AE activity was analyzed, presuming that AE occurs when plastic deformation and fracturing of metallic materials occur. For the analysis, a threshold voltage is set to distinguish the AE signal from the ripple voltage signal and noise. If the amplitude of the AE signal is small, it is difficult to distinguish the AE signal from the ripple voltage signal and the noise signal. Hence, there is a limitation in predicting fractures using the AE sensor. To overcome this limitation, the Kalman filter was used in this study to remove the ripple voltage signal and noise signal and then analyze the activity. However, it was difficult to filter out the ripple voltage signal using a conventional low-pass filter or Kalman filter because the ripple voltage signal is a high-frequency component governed by the switch-mode of the power supply. Therefore, a Kalman filter that has a low Kalman gain was designed to extract only the ripple voltage signal. Based on the KF-RV algorithm, the measured ripple voltage and noise signal were reduced by 97.3% on average. Subsequently, the AE signal was extracted appropriately using the difference between the measured value and the extracted ripple voltage signal. The activity of the extracted AE signal was analyzed using the ring-down count among various AE parameters to determine if there was a fracture in the test specimen. Full article

(This article belongs to the Special Issue Acoustic Emission Sensors for Structural Health Monitoring)

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14 pages, 3561 KiB

Open AccessArticle

Elastic Wave Monitoring of Cementitious Mixtures Including Internal Curing Mechanisms

by Gerlinde Lefever, Didier Snoeck, Nele De Belie, Danny Van Hemelrijck and Dimitrios G. Aggelis

Sensors 2021, 21(7), 2463; https://doi.org/10.3390/s21072463 - 02 Apr 2021

Cited by 5 | Viewed by 1566

Abstract

The mitigation of autogenous shrinkage in cementitious materials by internal curing has been widely studied. By the inclusion of water reservoirs, in form of saturated lightweight aggregates or superabsorbent polymers, additional water is provided to the hydrating matrix. The onset of water release is of high importance and determines the efficiency of the internal curing mechanism. However, the monitoring of it poses problems as it is a process that takes place in the microstructure. Using acoustic emission (AE) sensors, the internal curing process is monitored, revealing its initiation and intensity, as well as the duration. In addition, AE is able to capture the water evaporation from saturated specimens. By ultrasonic testing, differences in the hydration kinetics are observed imposed by the different methods of internal curing. The results presented in this paper show the sensitivity of combined AE and ultrasound experiments to various fundamental mechanisms taking place inside cementitious materials and demonstrate the ability of acoustic emission to evaluate internal curing in a non-destructive and easily implementable way. Full article

(This article belongs to the Special Issue Acoustic Emission Sensors for Structural Health Monitoring)

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15 pages, 11557 KiB

Open AccessArticle

An Acoustic Sensor for Combined Sewer Overflow (CSO) Screen Condition Monitoring in a Drainage Infrastructure

by Chan H. See, Kirill V. Horoshenkov, M. Tareq Bin Ali and Simon J. Tait

Sensors 2021, 21(2), 404; https://doi.org/10.3390/s21020404 - 08 Jan 2021

Cited by 4 | Viewed by 3184

Abstract

Combined sewer overflow structures (CSO) play an important role in sewer networks. When the local capacity of a sewer system is exceeded during intense rainfall events, they act as a “safety valve” and discharge excess rainfall run-off and wastewater directly to a natural receiving water body, thus preventing widespread urban flooding. There is a regulatory requirement that solids in CSO spills must be small and their amount strictly controlled. Therefore, a vast majority of CSOs in the UK contain screens. This paper presents the results of a feasibility study of using low-cost, low-energy acoustic sensors to remotely assess the condition of CSO screens to move to cost-effective reactive maintenance visits. In situ trials were carried out in several CSOs to evaluate the performance of the acoustic sensor under realistic screen and flow conditions. The results demonstrate that the system is robust within ±2.5% to work successfully in a live CSO environment. The observed changes in the screen condition resulted in 8–39% changes in the values of the coefficient in the proposed acoustic model. These changes are detectable and consistent with observed screen and hydraulic data. This study suggested that acoustic-based sensing can effectively monitor the CSO screen blockage conditions and hence reduce the risk of non-compliant CSO spills. Full article

(This article belongs to the Special Issue Acoustic Emission Sensors for Structural Health Monitoring)

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Review

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29 pages, 3979 KiB

Open AccessReview

Cement-Based Piezoelectric Ceramic Composites for Sensing Elements: A Comprehensive State-of-the-Art Review

by Weijian Ding, Yuqing Liu, Tomoki Shiotani, Quan Wang, Ningxu Han and Feng Xing

Sensors 2021, 21(9), 3230; https://doi.org/10.3390/s21093230 - 07 May 2021

Cited by 19 | Viewed by 4218

Abstract

Compatibility, a critical issue between sensing material and host structure, significantly influences the detecting performance (e.g., sensitive, signal-to-noise ratio) of the embedded sensor. To address this issue in concrete-based infrastructural health monitoring, cement-based piezoelectric composites (piezoelectric ceramic particles as a function phase and cementitious materials as a matrix) have attracted continuous attention in the past two decades, dramatically exhibiting superior durability, sensitivity, and compatibility. This review paper performs a synthetical overview of recent advances in theoretical analysis, characterization and simulation, materials selection, the fabrication process, and application of the cement-based piezoelectric composites. The critical issues of each part are also presented. The influencing factors of the materials and fabrication process on the final performance of composites are further discussed. Meanwhile, the application of the composite as a sensing element for various monitoring techniques is summarized. Further study on the experiment and simulation, materials, fabrication technique, and application are also pointed out purposefully. Full article

(This article belongs to the Special Issue Acoustic Emission Sensors for Structural Health Monitoring)

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