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Sensors for NDT Diagnostics and 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 May 2021) | Viewed by 22664

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Special Issue Editors

Dr. Nico P. Avdelidis

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Guest Editor

Computer Vision and Systems Laboratory (CVSL), Department of Electrical and Computer Engineering, Laval University, Quebec City, QC G1V 0A6, Canada
Interests: NDT; health diagnostics; non-invasive imaging; autonomous systems inspections; composites; structures; thermal imaging; monitoring
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Antonios Tsourdos

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Guest Editor

Centre for Autonomous and Cyberphysical Systems, Cranfield University, Cranfield MK43 0AL, UK
Interests: unmanned aircraft systems; decision making on multi-agent systems; data-centric guidance and control; swarm
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The limitation of inspection processes on all sectors but mainly aviation, as they are labour-intensive and time-consuming, affecting operational efficiency, has led to automated real-time and continuous monitoring and diagnostics. This has managed to reduce operating costs and increase efficiency across the various sectors (transport, energy, etc.).

NDT diagnostics and health monitoring make use of emerging technologies in the fields of sensor technology, systems and control engineering, communications technology, and decision making. NDT diagnostics and health monitoring procedures must provide operators not only with the ability to detect defects, but also with the means to diagnose these defects for continuous monitoring and health management. For this reason, a large amount of work has been carried out focusing on the development of such procedures.

This Special Issue focuses on fostering improvements and new developments of technology in areas related to novel NDT diagnostics and health monitoring, including use of advanced smart sensors and sensor networks, as well as strategies for data utilisation for overall system safety and health management. We would like to invite original research articles, as well as review articles that contain theoretical, analytical, and experimental investigations covering all aspects of sensors for NDT diagnostics and health monitoring. Potential topics include but are not limited to:

Sensor technologies for NDT diagnostics and health monitoring, sensor networks and smart systems for evaluation, detection, monitoring, control and health management of transport systems;
Continuous NDT and E diagnostics and real-time results for improving measurement accuracy and/or inspection technologies;
Integration of multiple NDT diagnostics and health monitoring approaches for improving interpretation of results;
Modelling, simulation and technology development concerning health diagnostics and health management at multiple scales ranging from nano- and micro- scales to realistic structures;
Signal and/or image processing, data fusion, and energy harvesting for NDT diagnostics and sensor monitoring;
Interdisciplinary approaches and applications for sensors for NDT diagnostics and health monitoring.

Prof. Dr. Nicolas P. Avdelidis
Prof. Dr. Antonios Tsourdos
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

nondestructive
sensors
structural health monitoring
diagnostics
non-invasive imaging
health management

Published Papers (6 papers)

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Research

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17 pages, 2827 KiB

Open AccessArticle

Health Condition Estimation of Bearings with Multiple Faults by a Composite Learning-Based Approach

by Udeme Inyang, Ivan Petrunin and Ian Jennions

Sensors 2021, 21(13), 4424; https://0-doi-org.brum.beds.ac.uk/10.3390/s21134424 - 28 Jun 2021

Cited by 2 | Viewed by 2430

Abstract

Bearings are critical components found in most rotating machinery; their health condition is of immense importance to many industries. The varied conditions and environments in which bearings operate make them prone to single and multiple faults. Widespread interest in the improvements of single fault diagnosis meant limited attention was spent on multiple fault diagnosis. However, multiple fault diagnosis poses extra challenges due to the submergence of the weak fault by the strong fault, presence of non-Gaussian noise, coupling of the frequency components, etc. A number of existing convolutional neural network models operate on a distinct feature that is not enough to assure reliable results in the presence of these challenges. In this paper, extended feature sets in three homogenous deep learning models are used for multiple fault diagnosis. This ensures a measure of diversity is introduced to the health management dataset to obtain complementary solutions from the models. The outputs of the models are fused through blending ensemble learning. Experiments using vibration datasets based on bearing multiple faults show an accuracy of 98.54%, with an improvement of 2.74% in the overall effectiveness over the single models. Compared with other technologies, the results show that this approach provides an improved generalized diagnostic capability. Full article

(This article belongs to the Special Issue Sensors for NDT Diagnostics and Health Monitoring)

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34 pages, 73285 KiB

Open AccessArticle

Transmission Sensitivities of Contact Ultrasonic Transducers and Their Applications

by Kanji Ono, Hideo Cho, Hartmut Vallen and Robert T. M’Closkey

Sensors 2021, 21(13), 4396; https://0-doi-org.brum.beds.ac.uk/10.3390/s21134396 - 27 Jun 2021

Cited by 6 | Viewed by 2884

Abstract

In all ultrasonic material evaluation methods, transducers and sensors play a key role of mechanoelectrical conversion. Their transduction characteristics must be known quantitatively in designing and implementing successful structural health monitoring (SHM) systems. Yet, their calibration and verification have lagged behind most other aspects of SHM system development. This study aims to extend recent advances in quantifying the transmission and receiving sensitivities to normally incident longitudinal waves of ultrasonic transducers and acoustic emission sensors. This paper covers extending the range of detection to lower frequencies, expanding to areal and multiple sensing methods and examining transducer loading effects. Using the refined transmission characteristics, the receiving sensitivities of transducers and sensors were reexamined under the conditions representing their actual usage. Results confirm that the interfacial wave transmission is governed by wave propagation theory and that the receiving sensitivity of resonant acoustic emission sensors peaks at antiresonance. Full article

(This article belongs to the Special Issue Sensors for NDT Diagnostics and Health Monitoring)

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16 pages, 724 KiB

Open AccessArticle

Wind Turbine Gearbox Condition Monitoring Based on Class of Support Vector Regression Models and Residual Analysis

by Harsh S. Dhiman, Dipankar Deb, James Carroll, Vlad Muresan and Mihaela-Ligia Unguresan

Sensors 2020, 20(23), 6742; https://0-doi-org.brum.beds.ac.uk/10.3390/s20236742 - 25 Nov 2020

Cited by 31 | Viewed by 3689

Abstract

The intelligent condition monitoring of wind turbines reduces their downtime and increases reliability. In this manuscript, a feature selection-based methodology that essentially works on regression models is used for identifying faulty scenarios. Supervisory control and data acquisition (SCADA) data with 1009 samples from one year and one month before failure are considered. Gearbox oil and bearing temperatures are treated as target variables with all the other variables used for the prediction model. Neighborhood component analysis (NCA) as a feature selection technique is employed to select the best features and prediction performance for several machine learning regression models is assessed. The results reveal that twin support vector regression (99.91%) and decision trees (98.74%) yield the highest accuracy for gearbox oil and bearing temperatures respectively. It is observed that NCA increases the accuracy and thus reliability of the condition monitoring system. Furthermore, the residuals from the class of support vector regression (SVR) models are tested from a statistical point of view. Diebold–Mariano and Durbin–Watson tests are carried out to establish the robustness of the tested models. Full article

(This article belongs to the Special Issue Sensors for NDT Diagnostics and Health Monitoring)

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19 pages, 3270 KiB

Open AccessArticle

Structural Monitoring of Underground Structures in Multi-Layer Media by Dynamic Methods

by Alexandr Lyapin, Alexey Beskopylny and Besarion Meskhi

Sensors 2020, 20(18), 5241; https://0-doi-org.brum.beds.ac.uk/10.3390/s20185241 - 14 Sep 2020

Cited by 16 | Viewed by 2672

Abstract

The actual problem of structural monitoring and modeling of dynamic response from buried building is considered in the framework of arbitrary dynamic load. The results can be used for designing underground transport constructions, crossings, buried reservoirs and foundations. In existing methods, the system of sensors that register the response to a dynamic action does not allow for effective interpretation of the signal without understanding the dynamic features and resonance phenomena. The analytical and numerical solution of the problem of the dynamics of a buried object in a layered medium is considered. A multilayer half-space is a set of rigidly interconnected layers characterized by elastic properties. At a distance, an arbitrary dynamic load acts on the half-space, which causes oscillations in the embedded structure, and the sensor system registers the response. The problem of assessing the dynamic stress-strain state (DSSS) is solved using Fourier transforms with the principle of limiting absorption. As an example, the behavior of an embedded massive structure of an underground pedestrian crossing under the influence of a dynamic surface source on a multilayer medium is considered, as well as instrumental support of the sensor system. The solution in the form of stress, strain and displacement fields is obtained and compared with the experimental data. The frequency-dependent characteristics of the system are determined and the possibility of determining the DSSS by a shock pulse is shown. Full article

(This article belongs to the Special Issue Sensors for NDT Diagnostics and Health Monitoring)

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16 pages, 5311 KiB

Open AccessArticle

A Novel Infrared Thermography Sensing Approach for Rapid, Quantitative Assessment of Damage in Aircraft Composites

by Spyridoula Farmaki, Dimitrios A. Exarchos, Ilias K. Tragazikis, Theodore E. Matikas and Konstantinos G. Dassios

Sensors 2020, 20(15), 4113; https://0-doi-org.brum.beds.ac.uk/10.3390/s20154113 - 24 Jul 2020

Cited by 16 | Viewed by 3390

Abstract

The current necessity of the scientific and industrial community, for reduction of aircraft maintenance cost and duration, prioritizes the need for development of innovative nondestructive techniques enabling fast and reliable defect detection on aircraft fuselage and wing skin parts. Herein, a new low-cost thermographic strategy, termed Pulsed Phase-Informed Lock-in Thermography, operating on the synergy of two independent, active infrared thermography techniques, is reported for the fast and quantitative assessment of superficial and subsurface damage in aircraft-grade composite materials. The two-step approach relies on the fast, initial qualitative assessment, by Pulsed Phase Thermography, of defect location and the identification of the optimal material-intrinsic frequency, over which lock-in thermography is subsequently applied for the quantification of the damage’s dilatational characteristics. A state-of-the-art ultra-compact infrared thermography module envisioned to form part of a fully-automated autonomous nondestructive testing inspection solution for aircraft was conceived, developed, and tested on aircraft-grade composite specimens with impact damages induced at variable energy levels and on a full-scale aircraft fuselage skin composite panel. The latter task was performed in semi-automated mode with the infrared thermography module mounted on the prototype autonomous vortex robot platform. The timescale requirement for a full assessment of damage(s) within the sensor’s field of view is of the order of 60 s which, in combination with the high precision of the methodology, unfolds unprecedented potential towards the reduction in duration and costs of tactical aircraft maintenance, optimization of efficiency and minimization of accidents. Full article

(This article belongs to the Special Issue Sensors for NDT Diagnostics and Health Monitoring)

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Other

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29 pages, 20026 KiB

Open AccessProject Report

Comparison of Cooled and Uncooled IR Sensors by Means of Signal-to-Noise Ratio for NDT Diagnostics of Aerospace Grade Composites

by Shakeb Deane, Nicolas P. Avdelidis, Clemente Ibarra-Castanedo, Hai Zhang, Hamed Yazdani Nezhad, Alex A. Williamson, Tim Mackley, Xavier Maldague, Antonios Tsourdos and Parham Nooralishahi

Sensors 2020, 20(12), 3381; https://0-doi-org.brum.beds.ac.uk/10.3390/s20123381 - 15 Jun 2020

Cited by 35 | Viewed by 6605

Abstract

This work aims to address the effectiveness and challenges of non-destructive testing (NDT) by active infrared thermography (IRT) for the inspection of aerospace-grade composite samples and seeks to compare uncooled and cooled thermal cameras using the signal-to-noise ratio (SNR) as a performance parameter. It focuses on locating impact damages and optimising the results using several signal processing techniques. The work successfully compares both types of cameras using seven different SNR definitions, to understand if a lower-resolution uncooled IR camera can achieve an acceptable NDT standard. Due to most uncooled cameras being small, lightweight, and cheap, they are more accessible to use on an unmanned aerial vehicle (UAV). The concept of using a UAV for NDT on a composite wing is explored, and the UAV is also tracked using a localisation system to observe the exact movement in millimetres and how it affects the thermal data. It was observed that an NDT UAV can access difficult areas and, therefore, can be suggested for significant reduction of time and cost. Full article

(This article belongs to the Special Issue Sensors for NDT Diagnostics and Health Monitoring)

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