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Structural Health Monitoring with Acoustic Emission Sensors
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Structural Health Monitoring with Acoustic Emission Sensors

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

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 13858

Special Issue Editors

Dr. Vassilios Kappatos

E-Mail Website
Guest Editor
Hellenic Institute of Transport (HIT), Center for Research and Technology Hellas (CERTH), 57001 Thermi, Greece
Interests: wireless networking for infrastructure monitoring; wireless sensor deployment; wireless networks for SHM; real world wireless applications
Special Issues, Collections and Topics in MDPI journals
Dr. Mayorkinos Papaelias

E-Mail Website
Guest Editor
School of Metallurgy and Materials, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
Interests: non-destructive evaluation; structural health monitoring; condition monitoring; machine learning; digital twins
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Acoustic emission is extensively used for evaluation of the structural integrity of critical structures and condition monitoring of machineries under actual operational conditions. Acoustic emission technology is applied widely in a range of industries for the rapid assessment of key assets with the number of applications growing with time.

The aim of this Special Issue is to compile a set of breakthrough studies concerned with innovative research and development on the wider spectrum of acoustic emission technology. Papers addressing new insights in the research, development, applications, and operational benefits of acoustic emission are welcome. Articles may include, but are not limited to the following topics:

  • Research in novel acoustic emission sensors, including fiber optic acoustic emission sensors
  • Research on high-temperature acoustic emission applications
  • Application of acoustic emission techniques in challenging operational environments
  • Novel approaches for acoustic emission data acquisition and processing
  • New trends and applications of acoustic emission technology
  • Case studies of the application of acoustic emission in an industrial environment
  • Quantitative acoustic emission techniques that aim to accurately evaluate the level of damage accumulated by an asset being monitored

Dr. Vassilios Kappatos
Dr. Mayorkinos Papaelias
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. Sensors 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 2600 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

  • acoustic emission
  • structural health monitoring
  • condition monitoring
  • sensors
  • data processing

Published Papers (5 papers)

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Research

17 pages, 16472 KiB  
Article
Characterization of a Piezoelectric Acoustic Sensor Fabricated for Low-Frequency Applications: A Comparative Study of Three Methods
by María Campo-Valera, Rafael Asorey-Cacheda, Ignacio Rodríguez-Rodríguez and Isidro Villó-Pérez
Sensors 2023, 23(5), 2742; https://doi.org/10.3390/s23052742 - 02 Mar 2023
Cited by 4 | Viewed by 2380
Abstract
Piezoelectric transducers are widely used for generating acoustic energy, and choosing the right radiating element is crucial for efficient energy conversion. In recent decades, numerous studies have been conducted to characterize ceramics based on their elastic, dielectric, and electromechanical properties, which have improved [...] Read more.
Piezoelectric transducers are widely used for generating acoustic energy, and choosing the right radiating element is crucial for efficient energy conversion. In recent decades, numerous studies have been conducted to characterize ceramics based on their elastic, dielectric, and electromechanical properties, which have improved our understanding of their vibrational behavior and aided in the manufacturing of piezoelectric transducers for ultrasonic applications. However, most of these studies have focused on the characterization of ceramics and transducers using electrical impedance to obtain resonance and anti-resonance frequencies. Few studies have explored other important quantities such as acoustic sensitivity using the direct comparison method. In this work, we present a comprehensive study that covers the design, manufacturing, and experimental validation of a small-sized, easy-to-assemble piezoelectric acoustic sensor for low-frequency applications, using a soft ceramic PIC255 from PI Ceramic with a diameter of 10 mm and a thickness of 5 mm. We present two methods, analytical and numerical, for sensor design, followed by experimental validation, allowing for a direct comparison of measurements with simulated results. This work provides a useful evaluation and characterization tool for future applications of ultrasonic measurement systems. Full article
(This article belongs to the Special Issue Structural Health Monitoring with Acoustic Emission Sensors)
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14 pages, 2195 KiB  
Article
Sound Range AE as a Tool for Diagnostics of Large Technical and Natural Objects
by Yuri Marapulets, Alexandra Solodchuk, Olga Lukovenkova, Mikhail Mishchenko and Albert Shcherbina
Sensors 2023, 23(3), 1269; https://doi.org/10.3390/s23031269 - 22 Jan 2023
Viewed by 1064
Abstract
Application of acoustic emission of the sound frequency range is under consideration. This range is of current interest for the diagnostics of the stability of mountain slopes, glaciers, ice covers, large technical constructions (bridges, dams, etc.) as well as for the detection of [...] Read more.
Application of acoustic emission of the sound frequency range is under consideration. This range is of current interest for the diagnostics of the stability of mountain slopes, glaciers, ice covers, large technical constructions (bridges, dams, etc.) as well as for the detection of rock deformation anomalies preceding earthquakes. Acoustic sensors, which can be used to record and to determine the directivity of acoustic emission of the sound frequency range, are under consideration. The structure of the system for acoustic emission recording, processing and analysis is described. This system makes it possible to determine the direction to the acoustic emission source using one multi-component sensor. We also consider the algorithms for detection of acoustic emission pulses in a noisy background, and for the analysis of their structure using the Adaptive Matching Pursuit algorithm. A method for the detection of the direction to an acoustic emission signal source based on multi-component sensors is described. The results of application of sound range acoustic emission for the detection of the intensification of rock deformations, associated with earthquake preparation and development in the seismically active region of Kamchatka peninsula, are presented. Full article
(This article belongs to the Special Issue Structural Health Monitoring with Acoustic Emission Sensors)
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17 pages, 7580 KiB  
Article
Non-Contact Acoustic Emission Monitoring of Corrosion under Marine Growth
by Sarjoon Alkhateeb, Filippo Riccioli, Felipe Leon Morales and Lotfollah Pahlavan
Sensors 2023, 23(1), 161; https://doi.org/10.3390/s23010161 - 23 Dec 2022
Cited by 3 | Viewed by 1922
Abstract
Offshore support structures and mooring systems are predominantly subject to corrosion and fatigue. These structures are typically covered with marine growth of various types. Conventional inspection methods for assessment of the structural integrity require access to the cleaned surface of these structures; however, [...] Read more.
Offshore support structures and mooring systems are predominantly subject to corrosion and fatigue. These structures are typically covered with marine growth of various types. Conventional inspection methods for assessment of the structural integrity require access to the cleaned surface of these structures; however, the cleaning process is highly undesirable from the technical, economical, and environmental points of view. This paper highlights research on feasibility assessment of detection and localization of corrosion damage under marine growth using acoustic emission (AE). Experiments were conducted on two carbon steel plates, one baseline sample and one covered with artificially fabricated marine growth. The results of accelerated corrosion experiments suggest that corrosion-induced ultrasound signals can be detected with satisfactory signal-to-noise ratio using non-contact AE sensors. Ultrasound waves passing through marine growth showed around 12 dB drop in amplitude when compared to the base plate. A localization algorithm for corrosion induced-ultrasound signals was successfully implemented. Full article
(This article belongs to the Special Issue Structural Health Monitoring with Acoustic Emission Sensors)
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19 pages, 5152 KiB  
Article
Early Detection of Subsurface Fatigue Cracks in Rolling Element Bearings by the Knowledge-Based Analysis of Acoustic Emission
by Einar Løvli Hidle, Rune Harald Hestmo, Ove Sagen Adsen, Hans Lange and Alexei Vinogradov
Sensors 2022, 22(14), 5187; https://doi.org/10.3390/s22145187 - 11 Jul 2022
Cited by 4 | Viewed by 2406
Abstract
Aiming at early detection of subsurface cracks induced by contact fatigue in rotating machinery, the knowledge-based data analysis algorithm is proposed for health condition monitoring through the analysis of acoustic emission (AE) time series. A robust fault detector is proposed, and its effectiveness [...] Read more.
Aiming at early detection of subsurface cracks induced by contact fatigue in rotating machinery, the knowledge-based data analysis algorithm is proposed for health condition monitoring through the analysis of acoustic emission (AE) time series. A robust fault detector is proposed, and its effectiveness was demonstrated for the long-term durability test of a roller made of case-hardened steel. The reliability of subsurface crack detection was proven using independent ultrasonic inspections carried out periodically during the test. Subsurface cracks as small as 0.5 mm were identified, and their steady growth was tracked by the proposed AE technique. Challenges and perspectives of the proposed methodology are unveiled and discussed. Full article
(This article belongs to the Special Issue Structural Health Monitoring with Acoustic Emission Sensors)
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16 pages, 5112 KiB  
Article
A Novel Machine Learning-Based Methodology for Tool Wear Prediction Using Acoustic Emission Signals
by Juan Luis Ferrando Chacón, Telmo Fernández de Barrena, Ander García, Mikel Sáez de Buruaga, Xabier Badiola and Javier Vicente
Sensors 2021, 21(17), 5984; https://doi.org/10.3390/s21175984 - 06 Sep 2021
Cited by 30 | Viewed by 4205
Abstract
There is an increasing trend in the industry of knowing in real-time the condition of their assets. In particular, tool wear is a critical aspect, which requires real-time monitoring to reduce costs and scrap in machining processes. Traditionally, for the purpose of predicting [...] Read more.
There is an increasing trend in the industry of knowing in real-time the condition of their assets. In particular, tool wear is a critical aspect, which requires real-time monitoring to reduce costs and scrap in machining processes. Traditionally, for the purpose of predicting tool wear conditions in machining, mathematical models have been developed to extract the information from the signal of sensors attached to the machines. To reduce the complexity of developing physical models, where an in-depth knowledge of the system being modelled is required, the current trend is to use machine-learning (ML) models based on data from the tool wear. The acoustic emission (AE) technique has been widely used to capture data from and understand the real-time condition of industrial assets such as cutting tools. However, AE signal interpretation and processing is rather complex. One of the most common features extracted from AE signals to predict the tool wear is the counts parameter, defined as the number of times that the amplitude of the signal exceeds a predefined threshold. A recurrent problem of this feature is to define the adequate threshold to obtain consistent wear prediction. Additionally, AE signal bandwidth is rather wide, and the selection of the optimum frequencies band for feature extraction has been pointed out as critical and complex by many authors. To overcome these problems, this paper proposes a methodology that applies multi-threshold count feature extraction at multiresolution level using wavelet packet transform, which extracts a redundant and non-optimal feature map from the AE signal. Next, recursive feature elimination is performed to reduce and optimize the vast number of predicting features generated in the previous step, and random forests regression provides the estimated tool wear. The methodology presented was tested using data captured when turning 19NiMoCr6 steel under pre-established cutting conditions. The results obtained were compared with several ML algorithms such as k-nearest neighbors, support vector machines, artificial neural networks and decision trees. Experimental results show that the proposed method can reduce the predicted root mean squared error by 36.53%. Full article
(This article belongs to the Special Issue Structural Health Monitoring with Acoustic Emission Sensors)
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