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Materials (MDPI) Internal Stress Monitoring of In-Service Structural Steel Members with Ultrasonic Method Z. Li1 4, J. He1 4, J. Teng1 5, Y. Wang2 4 1School of Civil and Environment Engineering, Shenzhen Graduate School; Harbin Institute of Technology (HIT) 38, Harbin, China 2Department of Civil and Environmental Engineering; University of Surrey 10, Surrey, United Kingdom Ultrasonic Testing (UT), acoustoelasticity, Internal stresses, longitudinal critically refracted waves
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Internal stress in structural steel members is an important parameter for steel structures
in their design, construction, and service stages. However, it is hard to measure via traditional
approaches. Among the existing non-destructive testing (NDT) methods, the ultrasonic method has
received the most research attention. Longitudinal critically refracted (Lcr) waves, which propagate
parallel to the surface of the material within an effective depth, have shown great potential as an
effective stress measurement approach. This paper presents a systematic non-destructive evaluation
method to determine the internal stress in in-service structural steel members using Lcr waves. Based
on theory of acoustoelasticity, a stress evaluation formula is derived. Factor of stress to acoustic
time difference is used to describe the relationship between stress and measurable acoustic results.
A testing facility is developed and used to demonstrate the performance of the proposed method.
Two steel members are measured by using the proposed method and the traditional strain gauge
method for verification. Parametric studies are performed on three steel members and the aluminum
plate to investigate the factors that influence the testing results. The results show that the proposed
method is effective and accurate for determining stress in in-service structural steel members.
| Materials (MDPI) |
Sensors (MDPI) High Temperature Shear Horizontal Electromagnetic Acoustic Transducer for GuidedWave Inspection M. Kogia , T. Gan 30, W. Balachandran 6, M. Livadas 3, V. Kappatos 10, I. Szabo , A. Mohimi 3, A. Round Brunel Innovation Centre ((BIC); Brunel University 36, Uxbridge, United Kingdom Electromagnetic Acoustic Transducers, EMAT, Guided Wave Testing, high temperature inspection
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Guided Wave Testing (GWT) using novel Electromagnetic Acoustic Transducers (EMATs)
is proposed for the inspection of large structures operating at high temperatures. To date, high
temperature EMATs have been developed only for thickness measurements and they are not suitable
for GWT. A pair of water-cooled EMATs capable of exciting and receiving Shear Horizontal (SH0)
waves for GWT with optimal high temperature properties (up to 500 C) has been developed. Thermal
and Computational Fluid Dynamic (CFD) simulations of the EMAT design have been performed
and experimentally validated. The optimal thermal EMAT design, material selection and operating
conditions were calculated. The EMAT was successfully tested regarding its thermal and GWT
performance from ambient temperature to 500 C.
| Sensors (MDPI) |
Sensors (MDPI) A Practical and Portable Solids-State Electronic Terahertz Imaging System K. Smart1, J. Du1, L. Li1 , D. Wang1 , K. Leslie1, F. Ji2 , D. Zeng2 , X. Li2 1Commonwealth Scientific and Industrial Research Organisation (CSIRO) 2, Lindfield, Australia 2Chengdu Shuguang Optical Fiber Network Co., Ltd, Chengdu, China terahertz, imaging, solid-state electronic components
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A practical compact solid-state terahertz imaging system is presented. Various beam
guiding architectures were explored and hardware performance assessed to improve its compactness,
robustness, multi-functionality and simplicity of operation. The system performance in terms of
image resolution, signal-to-noise ratio, the electronic signal modulation versus optical chopper, is
evaluated and discussed. The system can be conveniently switched between transmission and
reflection mode according to the application. A range of imaging application scenarios was explored
and images of high visual quality were obtained in both transmission and reflection mode.
| Sensors (MDPI) |
Sensors (MDPI) Dynamic Measurement for the Diameter of A Train Wheel Based on Structured-Light Vision Z. Gong , J. Sun , G. Zhang Key Laboratory of Precision Opto-Mechatronics Technology, Ministry of Education; Beihang University (BUAA) 39, Beijing, China train wheel diameter, dynamic measurement, structured-light vision, machine vision
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Wheels are very important for the safety of a train. The diameter of the wheel is a significant
parameter that needs regular inspection. Traditional methods only use the contact points of the
wheel tread to fit the rolling round. However, the wheel tread is easily influenced by peeling or
scraping. Meanwhile, the circle fitting algorithm is sensitive to noise when only three points are used.
This paper proposes a dynamic measurement method based on structured-light vision. The axle of
the wheelset and the tread are both employed. The center of the rolling round is determined by the
axle rather than the tread only. Then, the diameter is calculated using the center and the contact
points together. Simulations are performed to help design the layout of the sensors, and the influences
of different noise sources are also analyzed. Static and field experiments are both performed, and the
results show it to be quite stable and accurate.
| Sensors (MDPI) |
Sensors (MDPI) Reducing Sweeping Frequencies in Microwave NDT Employing Machine Learning Feature Selection A. Moomen1 , A. Ali2 2, O. Ramahi2 2 1Department of Computer Science; Rochester Institute of Technology, Rochester, USA 2Electrical and Computer Engineering (ECE); University of Waterloo 18, Waterloo, Canada microwave sensors, nondestructive testing, feature selection, machine learning
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Nondestructive Testing (NDT) assessment of materials’ health condition is useful for
classifying healthy from unhealthy structures or detecting flaws in metallic or dielectric structures.
Performing structural health testing for coated/uncoated metallic or dielectric materials with the
same testing equipment requires a testing method that can work on metallics and dielectrics such
as microwave testing. Reducing complexity and expenses associated with current diagnostic
practices of microwave NDT of structural health requires an effective and intelligent approach
based on feature selection and classification techniques of machine learning. Current microwave
NDT methods in general based on measuring variation in the S-matrix over the entire operating
frequency ranges of the sensors. For instance, assessing the health of metallic structures using a
microwave sensor depends on the reflection or/and transmission coefficient measurements as a
function of the sweeping frequencies of the operating band. The aim of this work is reducing sweeping
frequencies using machine learning feature selection techniques. By treating sweeping frequencies
as features, the number of top important features can be identified, then only the most influential
features (frequencies) are considered when building the microwave NDT equipment. The proposed
method of reducing sweeping frequencies was validated experimentally using a waveguide sensor
and a metallic plate with different cracks. Among the investigated feature selection techniques
are information gain, gain ratio, relief, chi-squared. The effectiveness of the selected features
were validated through performance evaluations of various classification models; namely, Nearest
Neighbor, Neural Networks, Random Forest, and Support Vector Machine. Results showed good
crack classification accuracy rates after employing feature selection algorithms.
| Sensors (MDPI) |
Sensors (MDPI) Concrete Condition Assessment Using Impact-Echo Method and Extreme Learning Machines J. Zhang1 , Y. Weizhong2 , D. Cui1 1Anhui and Huaihe River Institute of Hydraulic Research, Bengbo, 2GE Global Research 4, Niskayuna, NY, USA defect detection, extreme learning machine, feature extraction, machine learning, nondestructive testing, wavelet transform
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The impact-echo (IE) method is a popular non-destructive testing (NDT) technique widely
used for measuring the thickness of plate-like structures and for detecting certain defects inside
concrete elements or structures. However, the IE method is not effective for full condition assessment
(i.e., defect detection, defect diagnosis, defect sizing and location), because the simple frequency
spectrum analysis involved in the existing IE method is not sufficient to capture the IE signal patterns
associated with different conditions. In this paper, we attempt to enhance the IE technique and enable
it for full condition assessment of concrete elements by introducing advanced machine learning
techniques for performing comprehensive analysis and pattern recognition of IE signals. Specifically,
we use wavelet decomposition for extracting signatures or features out of the raw IE signals and
apply extreme learning machine, one of the recently developed machine learning techniques, as
classification models for full condition assessment. To validate the capabilities of the proposed
method, we build a number of specimens with various types, sizes, and locations of defects and
perform IE testing on these specimens in a lab environment. Based on analysis of the collected IE
signals using the proposed machine learning based IE method, we demonstrate that the proposed
method is effective in performing full condition assessment of concrete elements or structures.
| Sensors (MDPI) |
Sensors (MDPI) Evaluation of SHM System Produced by Additive Manufacturing via Acoustic Emission and Other NDT Methods M. Strantza 5, D. Aggelis 27, D. De Baere 6, P. Guillaume 11, D. Van Hemelrijck 18 aDepartment of Mechanics of Materials and Constructions b2Department of Mechanical Engineering and Aeronautics cDepartment of Mechanical Engineering cDepartment Mechanics of Materials & Constructions (MeMC; Vrije Universiteit Brussel (VUB) 54, Brussel, Belgium acoustic emission, additive manufacturing, structural health monitoring, liquid penetrant inspection, radiography, eddy current
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During the last decades, structural health monitoring (SHM) systems are used in order to detect damage in structures. We have developed a novel structural health monitoring approach, the so-called “effective structural health monitoring” (eSHM) system. The current SHM system is incorporated into a metallic structure by means of additive manufacturing (AM) and has the possibility to advance life safety and reduce direct operative costs. It operates based on a network of capillaries that are integrated into an AM structure. The internal pressure of the capillaries is continuously monitored by a pressure sensor. When a crack nucleates and reaches the capillary, the internal pressure changes signifying the existence of the flaw. The main objective of this paper is to evaluate the crack detection capacity of the eSHM system and crack location accuracy by means of various non-destructive testing (NDT) techniques. During this study, detailed acoustic emission (AE) analysis was applied in AM materials for the first time in order to investigate if phenomena like the Kaiser effect and waveform parameters used in conventional metals can offer valuable insight into the damage accumulation of the AM structure as well. Liquid penetrant inspection, eddy current and radiography were also used
OPEN ACCESS
Sensors 2015, 15 26710
in order to confirm the fatigue damage and indicate the damage location on un-notched four-point bending AM metallic specimens with an integrated eSHM system. It is shown that the eSHM system in combination with NDT can provide correct information on the damage condition of additive manufactured metals.
| Sensors (MDPI) |
Sensors (MDPI) A Micro-Computed Tomography Technique to Study the Quality of Fibre Optics Embedded in Composite Materials G. Chiesura1, G. Luyckx1 5, E. Voet1 2, N. Lammens1 3, W. Van Paepegem1 7, J. Degrieck1 8, M. Dierick1 4, L. Van hoorebeke1 6, P. Vanderniepen1 , S. Sulejmani2 4, C. Sonnenfeld2 3, T. Geernaert2 6, F. Berghmans2 6 1aDepartment of Materials Science and Engineering bDept. Physics and Astronomy; University of Ghent (UGent) 51, Ghent, Belgium 2Brussels Photonics Team (B-PHOT); Vrije Universiteit Brussel (VUB) 54, Brussel, Belgium carbon fibre, defects, radiography, autoclave, prepreg
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Quality of embedment of optical fibre sensors in carbon fibre-reinforced
polymers plays an important role in the resultant properties of the composite, as well as
for the correct monitoring of the structure. Therefore, availability of a tool able to check
the optical fibre sensor-composite interaction becomes essential. High-resolution 3D X-ray
Micro-Computed Tomography, or Micro-CT, is a relatively new non-destructive inspection
technique which enables investigations of the internal structure of a sample without
actually compromising its integrity. In this work the feasibility of inspecting the position,
the orientation and, more generally, the quality of the embedment of an optical fibre sensor
in a carbon fibre reinforced laminate at unit cell level have been proven.
| Sensors (MDPI) |
Sensors (MDPI) Giant Magnetoresistance Sensors: A Review on Structures and Non-Destructive Eddy Current Testing Applications D. Rifai 2, A. Abdalla 2, K. Ali 2, R. Razali 2 Faculty of Engineering Technology; Universiti Malaysia Pahang 2, Pahang, Malaysia giant magnetoresistance, eddy current testing, non-destructive testing
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Non-destructive eddy current testing (ECT) is widely used to examine structural defects in
ferromagnetic pipe in the oil and gas industry. Implementation of giant magnetoresistance (GMR)
sensors as magnetic field sensors to detect the changes of magnetic field continuity have increased
the sensitivity of eddy current techniques in detecting the material defect profile. However, not many
researchers have described in detail the structure and issues of GMR sensors and their application
in eddy current techniques for nondestructive testing. This paper will describe the implementation
of GMR sensors in non-destructive testing eddy current testing. The first part of this paper will
describe the structure and principles of GMR sensors. The second part outlines the principles and
types of eddy current testing probe that have been studied and developed by previous researchers.
The influence of various parameters on the GMR measurement and a factor affecting in eddy current
testing will be described in detail in the third part of this paper. Finally, this paper will discuss the
limitations of coil probe and compensation techniques that researchers have applied in eddy current
testing probes. A comprehensive review of previous studies on the application of GMR sensors in
non-destructive eddy current testing also be given at the end of this paper.
| Sensors (MDPI) |
Sensors (MDPI) Detection and Inspection of Steel Bars in Reinforced
Concrete Structures Using Active Infrared Thermography with Microwave Excitation and Eddy Current Sensors B. Szymanik1 7, P. Frankowski1 , T. Chady1 10, C. Chelliah2 1Szczecin Department of Electrical and Computer Engineering; West Pomeranian University of Technology (ZUT) 18, Szczecin, Poland 2Department of Nanosciences and Technology, School of Science and Humanities; Karunya University 2, Coimbatore, India infrared thermography, microwave heating, eddy current testing, multi frequency eddy, current technique, concrete testing, rebar detection
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The purpose of this paper is to present a multi-sensor approach to the detection and
inspection of steel bars in reinforced concrete structures. In connection with our past experience
related to non-destructive testing of different materials, we propose using two potentially effective
methods: active infrared thermography with microwave excitation and the eddy current technique.
In this article active infrared thermography with microwave excitation is analyzed both by numerical
modeling and experiments. This method, based on thermal imaging, due to its characteriatics should
be considered as a preliminary method for the assessment of relatively shallowly located steel bar
reinforcements. The eddy current technique, on the other hand, allows for more detailed evaluation
and detection of deeply located rebars. In this paper a series of measurement results, together with
the initial identification of certain features of steel reinforcement bars will be presented.
| Sensors (MDPI) |
Ultrasonics (Elsevier) Multidimensional spectral analysis of the ultrasonic radiofrequency signal for characterization of media S. Granchi1, E. Vannacci1, E. Biagi1 , L. Masotti2 1Department of Information Engineering (DINFO); University of Florence 10, Florence, Italy 2Scientific Committee; El.En. S.p.A., Calenzano, Italy Ultrasonic Testing (UT), medical application, blood cells, spectral analysis, ultrasonic imaging, ultrasonic, k-mean clustering, test object, hyperspectral, filter function
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The importance of the analysis of the radiofrequency signal is by now recognized in the field of tissue
characterization via ultrasound. The RF signal contains a wealth of information and structural details that
are usually lost in the B-Mode representation. The HyperSPACE (Hyper SPectral Analysis for
Characterization in Echography) algorithm presented by the authors in previous papers for clinical applications
is based on the radiofrequency ultrasonic signal. The present work describes the method in detail
and evaluates its performance in a repeatable and standardized manner, by using two test objects: a commercial
test object that simulates the human parenchyma, and a laboratory-made test object consisting
of human blood at different dilution values. In particular, the sensitivity and specificity in discriminating
different density levels were estimated. In addition, the robustness of the algorithm with respect to the
signal-to-noise ratio was also evaluated.
| Ultrasonics (Elsevier) |
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