Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.7
Journals
Applied Sciences
Special Issues
Elastic Waves and Acoustic Emission for Innovative Monitoring of Structures...
share announcement

Elastic Waves and Acoustic Emission for Innovative Monitoring of Structures and Engineering Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Acoustics and Vibrations".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 51907

Special Issue Editors

Prof. Dr. Kanji Ono

E-Mail Website
Guest Editor
Department of Materials Science and Engineering, HSSEAS School of Engineering & Applied Sciences, University of California, Los Angeles, CA 90095, USA
Interests: acoustic emission; materials science
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Victor Giurgiutiu

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of South Carolina, Columbia, SC 29208, USA
Interests: structural health monitoring (SHM); nondestructive evaluation (NDE)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The present Special Issue focuses on elastic waves and acoustic emission (AE) for innovative uses in structural health monitoring (SHM) and condition monitoring of various engineering systems. Elastic wave propagation and acoustic emission phenomena have been widely utilized successfully for many years in the field of nondestructive evaluation (NDE), but today’s technology demands increasingly higher levels of performance. Newer approaches are essential to attain breakthrough achievements using modeling tools and emerging artificial intelligence (AI) technologies. The following topical areas are listed as examples, and other synergic efforts are most welcome:

  • Elastic wave methods with attenuation and dispersion for SHM;
  • AE applications to large and small structures;
  • Structural evaluation under extreme environments;
  • NDE methods merging AI with elastic waves and AE;
  • Feature analysis of crack-related AE signals;
  • Remote monitoring for elastic waves and AE methods;
  • Clustering methods for crack growth–crack faying discrimination;
  • Sensor technology and wireless systems;
  • AE for quality control processes;
  • Elastic waves and AE methods with damage and fracture mechanics;
  • Correlation of AE signal features to crack length.

Prof. Dr. Kanji Ono
Prof. Dr. Victor Giurgiutiu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Elastic wave NDE methods
  • Acoustic emission (AE)
  • Structural health monitoring (SHM)
  • Structural evaluation; crack signal discrimination
  • Extreme environments
  • NDE with Artificial Intelligence
  • Remote monitoring
  • Sensor and wireless technology
  • AE for quality control

Published Papers (15 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

24 pages, 19812 KiB  
Article
Acoustic Emission Monitoring in Prestressed Concrete: A Comparative Study of Signal Attenuation from Wire Breaks and Rebound Hammer Impulses
by Max Käding and Steffen Marx
Appl. Sci. 2024, 14(7), 3045; https://0-doi-org.brum.beds.ac.uk/10.3390/app14073045 - 04 Apr 2024
Viewed by 403
Abstract
Acoustic emission monitoring (AEM) has emerged as an effective technique for detecting wire breaks resulting from, e.g., stress corrosion cracking, and its application on prestressed concrete bridges is increasing. The success of this monitoring measure depends crucially on a carefully designed sensor layout. [...] Read more.
Acoustic emission monitoring (AEM) has emerged as an effective technique for detecting wire breaks resulting from, e.g., stress corrosion cracking, and its application on prestressed concrete bridges is increasing. The success of this monitoring measure depends crucially on a carefully designed sensor layout. For this, the attenuation of elastic waves within the structure’s material is ideally determined in situ through object-related measurements (ORMs) with a reproducible signal source, typically a rebound hammer. This assumes that the attenuation coefficients derived from rebound hammer tests are comparable to those from wire breaks, thus allowing their results to be directly applied to wire break detection without further adjustments. This study challenges this assumption by analysing attenuation behaviour through an extensive dataset. Employing time-domain and frequency analysis, the research generates attenuation profiles from laboratory experiments and in situ measurements across various girders and bridge structures, extracting the slope and residual standard deviation (RSD). While generally validating this approach, the findings highlight differences in attenuation behaviour from among wire break signals and rebound hammer impulses, whereby the latter potentially underestimates the relevant attenuation of wire breaks by approximately 20%. Consequently, a transfer factor is proposed to adjust ORM results obtained with the rebound hammer for wire break scenarios. It consists of a scaling factor of 1.2 to modify the average attenuation coefficient and a constant term of ±1.0 dB/m to cover a 95% confidence interval, and thus, account for sample scattering. Moreover, the anisotropic attenuation behaviour across different structures was studied, showing that transverse attenuation consistently exceeds the longitudinal, significantly influenced by structural features such as voids. In prefabricated concrete bridges with in situ-cast concrete slabs, transverse signal transmission remains unhindered across multiple elements. Finally, the results provide a valuable reference for the design of sensor layouts in bridge monitoring, particularly benefiting scenarios where direct in situ experiences are lacking. Full article
(This article belongs to the Special Issue Elastic Waves and Acoustic Emission for Innovative Monitoring of Structures and Engineering Systems)
►▼ Show Figures

Figure 1

13 pages, 4770 KiB  
Article
Acoustic Emission-Based Detection of Impacts on Thermoplastic Aircraft Control Surfaces: A Preliminary Study
by Li Ai, Sydney Flowers, Tanner Mesaric, Bryson Henderson, Sydney Houck and Paul Ziehl
Appl. Sci. 2023, 13(11), 6573; https://0-doi-org.brum.beds.ac.uk/10.3390/app13116573 - 29 May 2023
Cited by 2 | Viewed by 1416
Abstract
The reliability of aircraft control surfaces, constructed from thermoplastic materials, can be affected by impacts from airborne particles. Recognizing the exact position of such impacts is essential for correctly estimating the resulting damage. This research intended to address the issue by introducing an [...] Read more.
The reliability of aircraft control surfaces, constructed from thermoplastic materials, can be affected by impacts from airborne particles. Recognizing the exact position of such impacts is essential for correctly estimating the resulting damage. This research intended to address the issue by introducing an innovative structural health monitoring solution capable of autonomously detecting and localizing impacts using acoustic emission monitoring. The objective of this research is to investigate the application of AE for the localization of impacts on aircraft elevators using machine learning techniques, specifically regression algorithms. To achieve this goal, two algorithms, linear regression, and random forest, were employed for predicting the impact locations based on AE signals. The performance of each algorithm was validated on a thermoplastic composite aircraft elevator. Results indicated that both linear regression and random forest models show high accuracy in predicting the impact locations. The random forest model, with an R2 value of 0.98616 and an RMSE of 0.6778, outperformed the linear regression model, which exhibited an R2 value of 0.9361 and an RMSE of 1.4614. Full article
(This article belongs to the Special Issue Elastic Waves and Acoustic Emission for Innovative Monitoring of Structures and Engineering Systems)
►▼ Show Figures

Figure 1

19 pages, 6218 KiB  
Article
Improving the Performance of Convolutional GAN Using History-State Ensemble for Unsupervised Early Fault Detection with Acoustic Emission Signals
by Yu Wang and Alexey Vinogradov
Appl. Sci. 2023, 13(5), 3136; https://0-doi-org.brum.beds.ac.uk/10.3390/app13053136 - 28 Feb 2023
Cited by 3 | Viewed by 1322
Abstract
Early fault detection (EFD) in run-to-failure processes plays a crucial role in the condition monitoring of modern industrial rotating facilities, which entail increasing demands for safety, energy and ecological savings and efficiency. To enable effective protection measures, the evolving faults have to be [...] Read more.
Early fault detection (EFD) in run-to-failure processes plays a crucial role in the condition monitoring of modern industrial rotating facilities, which entail increasing demands for safety, energy and ecological savings and efficiency. To enable effective protection measures, the evolving faults have to be recognized and identified as early as possible. The major challenge is to distil discriminative features on the basis of only the ‘health’ signal, which is uniquely available from various possible sensors before damage sets in and before the signatures of incipient damage become obvious and well-understood in the signal. Acoustic emission (AE) signals have been frequently reported to be able to deliver early diagnostic information due to their inherently high sensitivity to the incipient fault activities, highlighting the great potential of the AE technique for EFD, which may outperform the traditional vibration-based analysis in many situations. To date, the ‘feature-based’ multivariate analysis dominates the interpretation of AE waveforms. In this way, the decision-making relies heavily on experts’ knowledge and experience, which is often a weak link in the entire EFD chain. With the advent of artificial intelligence, practitioners seek an intelligent method capable of tackling this challenge. In the present paper, we introduce a versatile approach towards intelligent data analysis adapted to AE signals streaming from the sensors used for the continuous monitoring of rotating machinery. A new architecture with a convolutional generative adversarial network (GAN) is designed to extract the deep information embedded in the AE waveforms. In order to improve the robustness of the proposed EFD framework, a novel ensemble technique referred to as ‘history-state ensemble’ (HSE) is introduced and paired with GAN. The primary merits of HSE are twofold: (1) it does not require extra computing time to obtain the base models, and (2) it does not require a special design of the network architecture and can be applied to different networks. To evaluate the proposed method, a durability rolling contact fatigue test was performed with the use of AE monitoring. The experimental results have demonstrated that the proposed ensemble method largely improves the robustness of GAN. Full article
(This article belongs to the Special Issue Elastic Waves and Acoustic Emission for Innovative Monitoring of Structures and Engineering Systems)
►▼ Show Figures

Figure 1

22 pages, 40099 KiB  
Article
Monitoring of Atmospheric Corrosion of Aircraft Aluminum Alloy AA2024 by Acoustic Emission Measurements
by Thomas Erlinger, Christoph Kralovec and Martin Schagerl
Appl. Sci. 2023, 13(1), 370; https://0-doi-org.brum.beds.ac.uk/10.3390/app13010370 - 27 Dec 2022
Cited by 1 | Viewed by 2085
Abstract
Atmospheric corrosion of aluminum aircraft structures occurs due to a variety of reasons. A typical phenomenon leading to corrosion during aircraft operation is the deliquescence of salt contaminants due to changes in the ambient relative humidity (RH). Currently, the corrosion of aircraft is [...] Read more.
Atmospheric corrosion of aluminum aircraft structures occurs due to a variety of reasons. A typical phenomenon leading to corrosion during aircraft operation is the deliquescence of salt contaminants due to changes in the ambient relative humidity (RH). Currently, the corrosion of aircraft is controlled through scheduled inspections. In contrast, the present contribution aims to continuously monitor atmospheric corrosion using the acoustic emission (AE) method, which could lead to a structural health monitoring application for aircraft. The AE method is frequently used for corrosion detection under immersion-like conditions or for corrosion where stress-induced cracking is involved. However, the applicability of the AE method to the detection of atmospheric corrosion in unloaded aluminum structures has not yet been demonstrated. To address this issue, the present investigation uses small droplets of a sodium chloride solution to induce atmospheric corrosion of uncladded aluminum alloy AA2024-T351. The operating conditions of an aircraft are simulated by controlled variations in the RH. The AE signals are measured while the corrosion site is visually observed through video recordings. A clear correlation between the formation and growth of pits, the AE and hydrogen bubble activity, and the RH is found. Thus, the findings demonstrate the applicability of the AE method to the monitoring of the atmospheric corrosion of aluminum aircraft structures using current measurement equipment. Numerous potential effects that can affect the measurable AE signals are discussed. Among these, bubble activity is considered to cause the most emissions. Full article
(This article belongs to the Special Issue Elastic Waves and Acoustic Emission for Innovative Monitoring of Structures and Engineering Systems)
►▼ Show Figures

Figure 1

41 pages, 13654 KiB  
Article
Experimental Determination of Lamb-Wave Attenuation Coefficients
by Kanji Ono
Appl. Sci. 2022, 12(13), 6735; https://0-doi-org.brum.beds.ac.uk/10.3390/app12136735 - 02 Jul 2022
Cited by 6 | Viewed by 1933
Abstract
This work determined the attenuation coefficients of Lamb waves of ten engineering materials and compared the results with calculated Lamb-wave attenuation coefficients, α–S and α–A. The Disperse program and a parametric method based on Disperse results were used for calculations. Bulk-wave attenuation coefficients, [...] Read more.
This work determined the attenuation coefficients of Lamb waves of ten engineering materials and compared the results with calculated Lamb-wave attenuation coefficients, α–S and α–A. The Disperse program and a parametric method based on Disperse results were used for calculations. Bulk-wave attenuation coefficients, αL and αT, were required as input parameters to the Disperse calculations. The calculated α–S and α–A values were found to be dominated by the αT contribution. Often α–Ao coincided with αT. The values of αL and αT were previously obtained or newly measured. Attenuation measurement relied on Lamb-wave generation by pulsed excitation of ultrasonic transducers and on surface-displacement detection with point contact receivers. The frequency used ranged from 10 kHz to 1 MHz. A total of 14 sheet and plate samples were evaluated. Sample materials ranged from steel, Al, and silicate glass with low attenuation to polymers and a fiber composite with much higher attenuation. Experimentally obtained Lamb-wave attenuation coefficients, α–S and α–A, for symmetric and asymmetric modes, were mostly for the zeroth mode. Plots of α–So and α–Ao values against frequency were found to coincide reasonably well to theoretically calculated curves. This study confirmed that the Disperse program predicts Lamb-wave attenuation coefficients for elastically isotropic materials within the limitation of the contact ultrasonic techniques used. Further refinements in experimental methods are needed, as large deviations often occurred, especially at low and high frequencies. Methods of refinement are suggested. Displacement measurements were quantified using Rayleigh wave calibration. For signals below 300 kHz, 1-mV receiver output corresponded to 1-pm displacement. Peak displacements after 200-mm propagation were found to range from 10 pm to 1.5 nm. With the use of signal averaging, the point-contact sensor was capable of detecting 1-pm displacement with 40 dB signal-to-noise ratio and had equivalent noise of 4.3 fm/√Hz. Approximate expressions for α–So and α–Ao were obtained, and an empirical correlation was found between bulk-wave attenuation coefficients, i.e., αT = 2.79 αL, for over 150 materials. Full article
(This article belongs to the Special Issue Elastic Waves and Acoustic Emission for Innovative Monitoring of Structures and Engineering Systems)
►▼ Show Figures

Figure 1

15 pages, 4675 KiB  
Article
Signal-Based Acoustic Emission Clustering for Differentiation of Damage Sources in Corroding Reinforced Concrete Beams
by Charlotte Van Steen and Els Verstrynge
Appl. Sci. 2022, 12(4), 2154; https://0-doi-org.brum.beds.ac.uk/10.3390/app12042154 - 18 Feb 2022
Cited by 7 | Viewed by 1463
Abstract
Corrosion in reinforced concrete (RC) structures is a major durability issue that requires attention in terms of monitoring, in order to assess the degraded condition and reduce financial costs for maintenance and repair. The acoustic emission (AE) technique has been found to be [...] Read more.
Corrosion in reinforced concrete (RC) structures is a major durability issue that requires attention in terms of monitoring, in order to assess the degraded condition and reduce financial costs for maintenance and repair. The acoustic emission (AE) technique has been found to be useful to monitor damage due to steel corrosion in RC. However, further development of monitoring protocols is still necessary towards on-site application. In this paper, a hierarchical clustering algorithm based on cross-correlation is developed and applied to automatically distinguish damage sources during the corrosion process. The algorithm is verified on dummy samples and corroding RC prisms. It is able to distinguish two clusters of which the first one containing AE signals due to corrosion, absorption, hydration, and micro-cracking, and the second one AE signals due to macro-cracking. Electromagnetic interference can be distinguished as a third cluster and filtered subsequently. Due to overlapping characteristics, further differentiation of the first cluster is not possible. Afterwards, the algorithm is scaled up to two sets of RC beams, one set with a uniform corrosion zone, and the other set with a local corrosion zone. In addition, on this sample scale, the algorithm is able to successfully differentiate macro-cracking from corrosion and micro-cracking. It can therefore serve as an additional tool to assess the extent of corrosion-induced damage. Full article
(This article belongs to the Special Issue Elastic Waves and Acoustic Emission for Innovative Monitoring of Structures and Engineering Systems)
►▼ Show Figures

Figure 1

17 pages, 4003 KiB  
Article
An Artificial Intelligence Approach to Fatigue Crack Length Estimation from Acoustic Emission Waves in Thin Metallic Plates
by Joseph Chandler Garrett, Hanfei Mei and Victor Giurgiutiu
Appl. Sci. 2022, 12(3), 1372; https://0-doi-org.brum.beds.ac.uk/10.3390/app12031372 - 27 Jan 2022
Cited by 15 | Viewed by 2688
Abstract
The acoustic emission (AE) technique has become a well-established method of monitoring structural health over recent years. The sensing and analysis of elastic AE waves, which have involved piezoelectric wafer active sensors (PWAS) and time domain and frequency domain analysis, has proven to [...] Read more.
The acoustic emission (AE) technique has become a well-established method of monitoring structural health over recent years. The sensing and analysis of elastic AE waves, which have involved piezoelectric wafer active sensors (PWAS) and time domain and frequency domain analysis, has proven to be effective in yielding fatigue crack-related information. However, not much research has been performed regarding (i) the correlation between the fatigue crack length and AE signal signatures and (ii) artificial intelligence (AI) methodologies to automate the AE waveform analysis. In this paper, this crack length correlation is investigated along with the development of a novel AE signal analysis technique via AI. A finite element model (FEM) study was first performed to understand the effects of fatigue crack length on the resulting AE waveforms and a fatigue experiment was performed to capture experimental AE waveforms. Finally, this database of experimental AE waveforms was used with a convolutional neural network to build a system capable of performing automated classification and prediction of the length of a fatigue crack that excited respective AE signals. AE signals captured during a fatigue crack growth experiment were found to match closely with the FEM simulations. This novel AI system proved to be effective at predicting the crack length of an AE signal at an accuracy of 98.4%. This novel AI-enabled AE signal analysis technique will provide a crucial step forward in the development of a comprehensive structural health monitoring (SHM) system. Full article
(This article belongs to the Special Issue Elastic Waves and Acoustic Emission for Innovative Monitoring of Structures and Engineering Systems)
►▼ Show Figures

Figure 1

18 pages, 7723 KiB  
Article
Empirical Approach to Defect Detection Probability by Acoustic Emission Testing
by Vera Barat, Artem Marchenkov, Valery Ivanov, Vladimir Bardakov, Sergey Elizarov and Alexander Machikhin
Appl. Sci. 2021, 11(20), 9429; https://0-doi-org.brum.beds.ac.uk/10.3390/app11209429 - 11 Oct 2021
Cited by 6 | Viewed by 1612
Abstract
Estimation of probability of defect detection (POD) is one of the most important problems in acoustic emission (AE) testing. It is caused by the influence of the material microstructure parameters on the diagnostic data, variability of noises, the ambiguous assessment of the materials [...] Read more.
Estimation of probability of defect detection (POD) is one of the most important problems in acoustic emission (AE) testing. It is caused by the influence of the material microstructure parameters on the diagnostic data, variability of noises, the ambiguous assessment of the materials emissivity, and other factors, which hamper modeling the AE data, as well as the a priori determination of the diagnostic parameters necessary for calculating POD. In this study, we propose an empirical approach based on the generalization of the experimental AE data acquired under mechanical testing of samples to a priori estimation of the AE signals emitted by the defect. We have studied the samples of common industrial steels 09G2S (similar to steel ANSI A 516-55) and 45 (similar to steel 1045) with fatigue cracks grown in laboratory conditions during cyclic testing. Empirical generalization of data using probabilistic models enables estimating the conditional probability of record emissivity and amplitudes of AE signals. This approach allows to eliminate the existing methodological gap and to build a comprehensive method for assessing the probability of fatigue cracks detection by the AE testing. Full article
(This article belongs to the Special Issue Elastic Waves and Acoustic Emission for Innovative Monitoring of Structures and Engineering Systems)
►▼ Show Figures

Figure 1

13 pages, 2862 KiB  
Article
Analytical Modeling of Acoustic Emission Signals in Thin-Walled Objects
by Vera Barat, Denis Terentyev, Vladimir Bardakov and Sergey Elizarov
Appl. Sci. 2020, 10(1), 279; https://0-doi-org.brum.beds.ac.uk/10.3390/app10010279 - 30 Dec 2019
Cited by 11 | Viewed by 2785
Abstract
For the effective detection of acoustic emission (AE) impulses against a noisy background, the correct assessment of AE parameters, and an increase in defect location accuracy during data processing are needed. For these goals, it is necessary to consider the waveform of the [...] Read more.
For the effective detection of acoustic emission (AE) impulses against a noisy background, the correct assessment of AE parameters, and an increase in defect location accuracy during data processing are needed. For these goals, it is necessary to consider the waveform of the AE impulse. The results of numerous studies have shown that the waveforms of AE impulses mainly depend on the properties of the waveguide, the path along which the signal propagates from the source to the sensor. In this paper, the analytical method for modeling of AE signals is considered. This model allows one to obtain model signals that have the same spectrum and waveform as real signals. Based on the obtained results, the attenuation parameters of the AE waves for various characteristics of the waveguide are obtained and the probability of defect detection at various distances between the AE source and sensor utilized for evaluation. Full article
(This article belongs to the Special Issue Elastic Waves and Acoustic Emission for Innovative Monitoring of Structures and Engineering Systems)
►▼ Show Figures

Figure 1

14 pages, 3715 KiB  
Article
A New Method of Low Amplitude Signal Detection and Its Application in Acoustic Emission
by Einar Agletdinov, Dmitry Merson and Alexei Vinogradov
Appl. Sci. 2020, 10(1), 73; https://0-doi-org.brum.beds.ac.uk/10.3390/app10010073 - 20 Dec 2019
Cited by 24 | Viewed by 3778
Abstract
A novel methodology is proposed to enhance the reliability of detection of low amplitude transients in a noisy time series. Such time series often arise in a wide range of practical situations where different sensors are used for condition monitoring of mechanical systems, [...] Read more.
A novel methodology is proposed to enhance the reliability of detection of low amplitude transients in a noisy time series. Such time series often arise in a wide range of practical situations where different sensors are used for condition monitoring of mechanical systems, integrity assessment of industrial facilities and/or microseismicity studies. In all these cases, the early and reliable detection of possible damage is of paramount importance and is practically limited by detectability of transient signals on the background of random noise. The proposed triggering algorithm is based on a logarithmic derivative of the power spectral density function. It was tested on the synthetic data, which mimics the actual ultrasonic acoustic emission signal recorded continuously with different signal-to-noise ratios (SNR). Considerable advantages of the proposed method over established fixed amplitude threshold and STA/LTA (Short Time Average / Long Time Average) techniques are demonstrated in comparative tests. Full article
(This article belongs to the Special Issue Elastic Waves and Acoustic Emission for Innovative Monitoring of Structures and Engineering Systems)
►▼ Show Figures

Figure 1

12 pages, 4027 KiB  
Article
Estimation of Fatigue Crack AE Emissivity Based on the Palmer–Heald Model
by Vera Barat, Artem Marchenkov and Sergey Elizarov
Appl. Sci. 2019, 9(22), 4851; https://0-doi-org.brum.beds.ac.uk/10.3390/app9224851 - 13 Nov 2019
Cited by 5 | Viewed by 2593
Abstract
This article is devoted to materials testing by the acoustic emission (AE) method, which is the analysis of models and diagnostic parameters to assess the probability of detection of a defect in steel structures. The paper proposes to evaluate the emissivity of the [...] Read more.
This article is devoted to materials testing by the acoustic emission (AE) method, which is the analysis of models and diagnostic parameters to assess the probability of detection of a defect in steel structures. The paper proposes to evaluate the emissivity of the material quantitatively by the number and dynamics of the accumulation of acoustic emission impulses. Experimental studies were carried out on pearlitic structural steels, including the loading of samples with fatigue cracks. It was established that the number of AE impulses emitted during loading of an object with a fatigue crack is a random variable corresponding to the normal distribution law. The results show that an estimate of the number of AE impulses emitted during the loading of samples with fatigue cracks can be obtained by distributing the multiplicative parameter D of the Palmer-Heald model by taking into account the maximum value of the applied load. Full article
(This article belongs to the Special Issue Elastic Waves and Acoustic Emission for Innovative Monitoring of Structures and Engineering Systems)
►▼ Show Figures

Figure 1

12 pages, 3325 KiB  
Article
Proposal of Laser-Induced Ultrasonic Guided Wave for Corrosion Detection of Reinforced Concrete Structures in Fukushima Daiichi Nuclear Power Plant Decommissioning Site
by Akinori Furusawa, Yusuke Takenaka and Akihiko Nishimura
Appl. Sci. 2019, 9(17), 3544; https://0-doi-org.brum.beds.ac.uk/10.3390/app9173544 - 29 Aug 2019
Cited by 10 | Viewed by 3668
Abstract
Remote-controlled, non-destructive testing is necessary to detect corrosion of the reinforced concrete structures at the Fukushima Daiichi Nuclear Power Plant (NPP) de-commissioning site. This work aims to demonstrate that laser-induced ultrasonic guided wave technology can be applied to achieve this task. Hence, accelerated [...] Read more.
Remote-controlled, non-destructive testing is necessary to detect corrosion of the reinforced concrete structures at the Fukushima Daiichi Nuclear Power Plant (NPP) de-commissioning site. This work aims to demonstrate that laser-induced ultrasonic guided wave technology can be applied to achieve this task. Hence, accelerated electrolytic corrosion is performed on a reinforced concrete specimen fabricated by embedding a steel rod into mortar. Waveforms of the laser-induced ultrasonic guided wave on the rod are measured with a previously employed piezoelectric transducer (PZT) probe, for each fixed corrosion time. Based on the results of Fourier and wavelet transforms of the waveforms, issues concerning the detection and extent of rebar corrosion are discussed. It is exhibited that the changes in bonding strength due to corrosion are distinguishable in the frequency domain of the ultrasonic signal. Full article
(This article belongs to the Special Issue Elastic Waves and Acoustic Emission for Innovative Monitoring of Structures and Engineering Systems)
►▼ Show Figures

Figure 1

Review

Jump to: Research

20 pages, 331 KiB  
Review
Structural Health and Condition Monitoring with Acoustic Emission and Guided Ultrasonic Waves: What about Long-Term Durability of Sensors, Sensor Coupling and Measurement Chain?
by Andreas J. Brunner
Appl. Sci. 2021, 11(24), 11648; https://0-doi-org.brum.beds.ac.uk/10.3390/app112411648 - 08 Dec 2021
Cited by 10 | Viewed by 3303
Abstract
Acoustic Emission (AE) and Guided Ultrasonic Waves (GUWs) are non-destructive testing (NDT) methods in several industrial sectors for, e.g., proof testing and periodic inspection of pressure vessels, storage tanks, pipes or pipelines and leak or corrosion detection. In materials research, AE and GUW [...] Read more.
Acoustic Emission (AE) and Guided Ultrasonic Waves (GUWs) are non-destructive testing (NDT) methods in several industrial sectors for, e.g., proof testing and periodic inspection of pressure vessels, storage tanks, pipes or pipelines and leak or corrosion detection. In materials research, AE and GUW are useful for characterizing damage accumulation and microscopic damage mechanisms. AE and GUW also show potential for long-term Structural Health and Condition Monitoring (SHM and CM). With increasing computational power, even online monitoring of industrial manufacturing processes has become feasible. Combined with Artificial Intelligence (AI) for analysis this may soon allow for efficient, automated online process control. AI also plays a role in predictive maintenance and cost optimization. Long-term SHM, CM and process control require sensor integration together with data acquisition equipment and possibly data analysis. This raises the question of the long-term durability of all components of the measurement system. So far, only scant quantitative data are available. This paper presents and discusses selected aspects of the long-term durability of sensor behavior, sensor coupling and measurement hardware and software. The aim is to identify research and development needs for reliable, cost-effective, long-term SHM and CM with AE and GUW under combined mechanical and environmental service loads. Full article
(This article belongs to the Special Issue Elastic Waves and Acoustic Emission for Innovative Monitoring of Structures and Engineering Systems)
14 pages, 2343 KiB  
Review
MEMS Acoustic Emission Sensors
by Didem Ozevin
Appl. Sci. 2020, 10(24), 8966; https://0-doi-org.brum.beds.ac.uk/10.3390/app10248966 - 16 Dec 2020
Cited by 21 | Viewed by 4495
Abstract
This paper presents a review of state-of-the-art micro-electro-mechanical-systems (MEMS) acoustic emission (AE) sensors. MEMS AE sensors are designed to detect active defects in materials with the transduction mechanisms of piezoresistivity, capacitance or piezoelectricity. The majority of MEMS AE sensors are designed as resonators [...] Read more.
This paper presents a review of state-of-the-art micro-electro-mechanical-systems (MEMS) acoustic emission (AE) sensors. MEMS AE sensors are designed to detect active defects in materials with the transduction mechanisms of piezoresistivity, capacitance or piezoelectricity. The majority of MEMS AE sensors are designed as resonators to improve the signal-to-noise ratio. The fundamental design variables of MEMS AE sensors include resonant frequency, bandwidth/quality factor and sensitivity. Micromachining methods have the flexibility to tune the sensor frequency to a particular range, which is important, as the frequency of AE signal depends on defect modes, constitutive properties and structural composition. This paper summarizes the properties of MEMS AE sensors, their design specifications and applications for detecting the simulated and real AE sources and discusses the future outlook. Full article
(This article belongs to the Special Issue Elastic Waves and Acoustic Emission for Innovative Monitoring of Structures and Engineering Systems)
►▼ Show Figures

Figure 1

52 pages, 7655 KiB  
Review
A Comprehensive Report on Ultrasonic Attenuation of Engineering Materials, Including Metals, Ceramics, Polymers, Fiber-Reinforced Composites, Wood, and Rocks
by Kanji Ono
Appl. Sci. 2020, 10(7), 2230; https://0-doi-org.brum.beds.ac.uk/10.3390/app10072230 - 25 Mar 2020
Cited by 65 | Viewed by 16444
Abstract
In this paper, ultrasonic attenuation of engineering materials is evaluated comprehensively, covering metals, ceramics, polymers, fiber-reinforced composites, wood, and rocks. After verifying two reliable experimental methods, 336 measurements are conducted and their results are tabulated. Attenuation behavior is determined over broadband spectra, extending [...] Read more.
In this paper, ultrasonic attenuation of engineering materials is evaluated comprehensively, covering metals, ceramics, polymers, fiber-reinforced composites, wood, and rocks. After verifying two reliable experimental methods, 336 measurements are conducted and their results are tabulated. Attenuation behavior is determined over broadband spectra, extending up to 15 MHz in low attenuating materials. The attenuation spectra are characterized in combination with four power law terms, with many showing linear frequency dependence, with or without Rayleigh scattering. Dislocation damping effects are re-evaluated and a new mechanism is proposed to explain some of the linear frequency dependencies. Additionally, quadratic and cubic dependencies due to Datta–Kinra scattering and Biwa scattering, respectively, are used for some materials to construct model relations. From many test results, some previously hidden behaviors emerged upon data evaluation. Effects of cold working, tempering, and annealing are complex and sometimes contradictory. Comparison to available literature was attempted for some, but most often prior data were unavailable. This collection of new attenuation data will be of value in materials selection and in designing structural health monitoring and non-destructive inspection protocols. Full article
(This article belongs to the Special Issue Elastic Waves and Acoustic Emission for Innovative Monitoring of Structures and Engineering Systems)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-15
Back to TopTop