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Advances in Sensors and Sensing for Technical Condition Assessment and NDT

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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 (15 December 2020) | Viewed by 41144

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Special Issue Editors

Prof. Dr. Tomasz Boczar

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Institute of Electric Power Engineering and Renewable Energy, Opole University of Technology, 45-758 Opole, Poland
Interests: issues related to electrical engineering; power engineering; renewable energy sources; automatic diagnostic methods of insulation systems of power equipment
Special Issues, Collections and Topics in MDPI journals

Dr. Michał Kunicki

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Guest Editor

Institute of Electrical Engineering and Renewable Energy, Faculty of Electrical Engineering Automatic Control and Informatics, Opole University of Technology, Prószkowska 76 Str., 45-758 Opole, Poland
Interests: measurements and metrology; technical diagnostics; non-destructive testing
Special Issues, Collections and Topics in MDPI journals

Dr. Daria Wotzka

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Guest Editor

Institute of Automatic Control, Faculty of Electrical Engineering Automatic Control and Informatics, Opole University of Technology, Prószkowska 76 Str., 45-758 Opole, Poland
Interests: acoustic emissions; infrasound; signal processing; machine learning

Dr. Łukasz Nagi

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Guest Editor

Institute of Electrical Engineering and Renewable Energy, Faculty of Electrical Engineering Automatic Control and Informatics, Opole University of Technology, Prószkowska 76 Str., 45-758 Opole, Poland
Interests: high-voltage techniques; radiation measurements; non-destructive testing; nanoparticles
Special Issues, Collections and Topics in MDPI journals

Dr. Michał Kozioł

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Guest Editor

Institute of Electric Power Engineering and Renewable Energy, Opole University of Technology, 45-758 Opole, Poland
Interests: high voltage technique; optical signals metrology and its analysis using machine learning methods algorithms; renewable energy technologies

Special Issue Information

Dear Colleagues,

The adequate condition assessment of key apparatus is one of the current hot issues regarding all branches of industry. Various different on-line and off-line diagnostic methods are widely applied in order to provide the early detection of any abnormality in exploitation. Furthermore, a number of different sensors may also be applied in order to capture selected physical quantities that may be used to indicate the type of the potential faults. The essential steps of the signal analysis regarding the technical condition assessment process may be listed as: signal measurement (using relevant sensor), processing, modelling, and classification.

With this in mind, we are launching a Special Issue entitled Advances in Sensors and Sensing for Technical Condition Assessment and NDT. We invite researchers to contribute high-quality original research or technical papers, reviews, and case studies to this Special Issue. Practical papers in which either examples of good present practice can be described and disseminated, or new proposals of improvements and applications of innovative solutions regarding sensors design and evaluation, signal measurements, processing, modeling, and classification (e.g., machine learning, artificial neural networks, clustering methods, etc.) are particularly sought. Theoretical papers of high technical merit relying on mathematical arguments and computation are also welcomed, but authors are asked to highlight and justify their potential industrial applications.

Prof. Dr. Tomasz Boczar
Dr. Michał Kunicki
Dr. Daria Wotzka
Dr. Łukasz Nagi
Dr. Michał Kozioł
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.

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Keywords

sensors and sensing
measurement science
technical condition assessment
non-destructive testing
classification and clustering methods
machine learning and artificial neural networks
signal processing, modelling, and simulation

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Published Papers (14 papers)

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Editorial

Jump to: Research, Other

3 pages, 162 KiB

Open AccessEditorial

Latest Trends in the Improvement of Measuring Methods and Equipment in the Area of NDT

by Daria Wotzka, Michał Kozioł, Tomasz Boczar, Michał Kunicki and Łukasz Nagi

Sensors 2021, 21(21), 7293; https://doi.org/10.3390/s21217293 - 2 Nov 2021

Cited by 3 | Viewed by 1411

Abstract

The adequate assessment of key apparatus conditions is a hot topic in all branches of industry [...] Full article

(This article belongs to the Special Issue Advances in Sensors and Sensing for Technical Condition Assessment and NDT)

Research

Jump to: Editorial, Other

17 pages, 4873 KiB

Open AccessArticle

Using a Current Shunt for the Purpose of High-Current Pulse Measurement

by Pawel Piekielny and Andrzej Waindok

Sensors 2021, 21(5), 1835; https://doi.org/10.3390/s21051835 - 6 Mar 2021

Cited by 6 | Viewed by 4132

Abstract

Measurement of high-current pulses is crucial in some special applications, e.g., electrodynamic accelerators (EA) and converters. In such cases, the current shunts have limitations concerning the frequency bandwidth. To overcome the problem, a method based on the shunt mathematical model is proposed. In the method, the solution of ordinary differential equations for the RL circuit is carried out in order to obtain the real current shape. To check the method, as a referee, a Rogowski coil dedicated to measuring high-current pulses was used. Additionally, the measurement results were compared with the mathematical model of the tested power supply system. Measurements were made for the short power supply circuit, which allows eliminating the nonlinearity. The calculations were carried out using a circuit model. In order to obtain the parameters of the shunt (resistance and inductance), it was modeled using an ANSYS/Q3D Extractor software. Comparison of calculation and measurement results confirms the correctness of our method. In order to compare results, the normalized root mean square error (NRMSE) was used. Full article

(This article belongs to the Special Issue Advances in Sensors and Sensing for Technical Condition Assessment and NDT)

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16 pages, 3792 KiB

Open AccessArticle

Thermal and Geometric Error Compensation Approach for an Optical Linear Encoder

by Donatas Gurauskis, Artūras Kilikevičius and Albinas Kasparaitis

Sensors 2021, 21(2), 360; https://doi.org/10.3390/s21020360 - 7 Jan 2021

Cited by 19 | Viewed by 3398

Abstract

Linear displacement measuring systems, like optical encoders, are widely used in various precise positioning applications to form a full closed-loop control system. Thus, the performance of the machine and the quality of its technological process are highly dependent on the accuracy of the linear encoder used. Thermoelastic deformation caused by a various thermal sources and the changing ambient temperature are important factors that introduce errors in an encoder reading. This work presents an experimental realization of the real-time geometric and thermal error compensation of the optical linear encoder. The implemented compensation model is based on the approximation of the tested encoder error by a simple parametric function and calculation of a linear nature error component according to an ambient temperature variation. The calculation of a two-dimensional compensation function and the real-time correction of the investigated linear encoder position readings are realized by using a field programmable gate array (FPGA) computing platform. The results of the performed experimental research verified that the final positioning error could be reduced up to 98%. Full article

(This article belongs to the Special Issue Advances in Sensors and Sensing for Technical Condition Assessment and NDT)

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23 pages, 12621 KiB

Open AccessArticle

Application of Correlation Analysis for Assessment of Infrasound Signals Emission by Wind Turbines

by Tomasz Boczar, Dariusz Zmarzły, Michał Kozioł and Daria Wotzka

Sensors 2020, 20(23), 6891; https://doi.org/10.3390/s20236891 - 2 Dec 2020

Cited by 6 | Viewed by 2658

Abstract

The study reported in this paper is concerned with areas related to developing methods of measuring, processing and analyzing infrasound noise caused by operation of wind farms. The paper contains the results of the correlation analysis of infrasound signals generated by a wind turbine with a rated capacity of 2 MW recorded by three independent measurement setups comprising identical components and characterized by the same technical parameters. The measurements of infrasound signals utilized a dedicated measurement system called INFRA, which was developed and built by KFB ACOUSTICS Sp. z o.o. In particular, the scope of the paper includes the results of correlation analysis in the time domain, which was carried out using the autocovariance function separately for each of the three measuring setups. Moreover, the courses of the cross-correlation function were calculated separately for each of the potential combinations of infrasound range recorded by the three measuring setups. In the second stage, a correlation analysis of the recorded infrasound signals in the frequency domain was performed, using the coherence function. In the next step, infrasound signals recorded in three setups were subjected to time-frequency transformations. In this part, the waveforms of the scalograms were determined by means of continuous wavelet transform. Wavelet coherence waveforms were calculated in order to determine the level of the correlation of the obtained dependencies in the time-frequency domain. The summary contains the results derived from using correlation analysis methods in the time, frequency and time-frequency domains. Full article

(This article belongs to the Special Issue Advances in Sensors and Sensing for Technical Condition Assessment and NDT)

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15 pages, 4206 KiB

Open AccessArticle

Vibroarthrographic Signal Spectral Features in 5-Class Knee Joint Classification

by Adam Łysiak, Anna Froń, Dawid Bączkowicz and Mirosław Szmajda

Sensors 2020, 20(17), 5015; https://doi.org/10.3390/s20175015 - 3 Sep 2020

Cited by 11 | Viewed by 3416

Abstract

Vibroarthrography (VAG) is a non-invasive and potentially widely available method supporting the joint diagnosis process. This research was conducted using VAG signals classified to five different condition classes: three stages of chondromalacia patellae, osteoarthritis, and control group (healthy knee joint). Ten new spectral features were proposed, distinguishing not only neighboring classes, but every class combination. Additionally, Frequency Range Maps were proposed as the frequency feature extraction visualization method. The results were compared to state-of-the-art frequency features using the Bhattacharyya coefficient and the set of ten different classification algorithms. All methods evaluating proposed features indicated the superiority of the new features compared to the state-of-the-art. In terms of Bhattacharyya coefficient, newly proposed features proved to be over 25% better, and the classification accuracy was on average 9% better. Full article

(This article belongs to the Special Issue Advances in Sensors and Sensing for Technical Condition Assessment and NDT)

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24 pages, 38343 KiB

Open AccessArticle

Evaluation of the Possibility of Identifying a Complex Polygonal Tram Track Layout Using Multiple Satellite Measurements

by Andrzej Wilk, Cezary Specht, Wladyslaw Koc, Krzysztof Karwowski, Jacek Skibicki, Jacek Szmagliński, Piotr Chrostowski, Pawel Dabrowski, Mariusz Specht, Marek Zienkiewicz, Slawomir Judek, Marcin Skóra and Sławomir Grulkowski

Sensors 2020, 20(16), 4408; https://doi.org/10.3390/s20164408 - 7 Aug 2020

Cited by 6 | Viewed by 2710

Abstract

We present the main assumptions about the algorithmization of the analysis of measurement data recorded in mobile satellite measurements. The research team from the Gdańsk University of Technology and the Maritime University in Gdynia, as part of a research project conducted in cooperation with PKP PLK (Polish Railway Infrastructure Manager), developed algorithms supporting the identification and assessment of track axis layout. This article presents selected issues concerning the identification of a tramway line’s axis system. For this purpose, the supporting algorithm was developed and measurement data recorded using Global Navigation Satellite System (GNSS) techniques was evaluated and analyzed. The discussed algorithm identifies main track directions from multi-device data and repeated position recordings. In order to observe the influence of crucial factors, the investigated route was carefully selected. The chosen tramway track was characterized by its location in various field conditions and a diversified and complex geometric layout. The analysis of the obtained results was focused on the assessment of the signal’s dispersion and repeatability using residuals in relation to the estimated track’s direction. The presented methodology is intended to support railway infrastructure management processes, mainly in planning and maintenance through an efficient inventory of the infrastructure in service. Full article

(This article belongs to the Special Issue Advances in Sensors and Sensing for Technical Condition Assessment and NDT)

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17 pages, 4778 KiB

Open AccessArticle

Distribution Transformer Parameters Detection Based on Low-Frequency Noise, Machine Learning Methods, and Evolutionary Algorithm

by Daniel Jancarczyk, Marcin Bernaś and Tomasz Boczar

Sensors 2020, 20(15), 4332; https://doi.org/10.3390/s20154332 - 4 Aug 2020

Cited by 8 | Viewed by 3757

Abstract

The paper proposes a method of automatic detection of parameters of a distribution transformer (model, type, and power) from a distance, based on its low-frequency noise spectra. The spectra are registered by sensors and processed by a method based on evolutionary algorithms and machine learning. The method, as input data, uses the frequency spectra of sound pressure levels generated during operation by transformers in the real environment. The model also uses the background characteristic to take under consideration the changing working conditions of the transformers. The method searches for frequency intervals and its resolution using both a classic genetic algorithm and particle swarm optimization. The interval selection was verified using five state-of-the-art machine learning algorithms. The research was conducted on 16 different distribution transformers. As a result, a method was proposed that allows the detection of a specific transformer model, its type, and its power with an accuracy greater than 84%, 99%, and 87%, respectively. The proposed optimization process using the genetic algorithm increased the accuracy by up to 5%, at the same time reducing the input data set significantly (from 80% up to 98%). The machine learning algorithms were selected, which were proven efficient for this task. Full article

(This article belongs to the Special Issue Advances in Sensors and Sensing for Technical Condition Assessment and NDT)

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20 pages, 9170 KiB

Open AccessArticle

Window-Modulated Compounding Nakagami Parameter Ratio Approach for Assessing Muscle Perfusion with Contrast-Enhanced Ultrasound Imaging

by Huang-Chen Lin and Shyh-Hau Wang

Sensors 2020, 20(12), 3584; https://doi.org/10.3390/s20123584 - 24 Jun 2020

Cited by 4 | Viewed by 2555

Abstract

The assessment of microvascular perfusion is essential for the diagnosis of a specific muscle disease. In comparison with the current available medical modalities, the contrast-enhanced ultrasound imaging is the simplest and fastest means for probing the tissue perfusion. Specifically, the perfusion parameters estimated from the ultrasound time-intensity curve (TIC) and statistics-based time–Nakagami parameter curve (TNC) approaches were found able to quantify the perfusion. However, due to insufficient tolerance on tissue clutters and subresolvable effects, these approaches remain short of reproducibility and robustness. Consequently, the window-modulated compounding (WMC) Nakagami parameter ratio imaging was proposed to alleviate these effects, by taking the ratio of WMC Nakagami parameters corresponding to the incidence of two different acoustic pressures from an employed transducer. The time–Nakagami parameter ratio curve (TNRC) approach was also developed to estimate perfusion parameters. Measurements for the assessment of muscle perfusion were performed from the flow phantom and animal subjects administrated with a bolus of ultrasound contrast agents. The TNRC approach demonstrated better sensitivity and tolerance of tissue clutters than those of TIC and TNC. The fusion image with the WMC Nakagami parameter ratio and B-mode images indicated that both the tissue structures and perfusion properties of ultrasound contrast agents may be better discerned. Full article

(This article belongs to the Special Issue Advances in Sensors and Sensing for Technical Condition Assessment and NDT)

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20 pages, 5384 KiB

Open AccessArticle

Study on the Influence of Measuring AE Sensor Type on the Effectiveness of OLTC Defect Classification

by Daria Wotzka and Andrzej Cichoń

Sensors 2020, 20(11), 3095; https://doi.org/10.3390/s20113095 - 30 May 2020

Cited by 14 | Viewed by 4093

Abstract

The principal objective of this study is to improve the diagnostics of power transformers, which are the key element of supplying electricity to consumers. On Load Tap Changer (OLTC), which is the object of research, the results of which are presented in this article, is one of the most important elements of these devices. The applied diagnostic method is the acoustic emission (AE) method, which has the main advantage over others, that it is considered as a non-destructive testing method. At present, there are many measuring devices and sensors used in the AE method, there are also some international standards, according to which, measurements should be performed. In the presented work, AE signals were measured in laboratory conditions with various OLTC defects being simulated. Five types of sensors were used for the measurement. The recorded signals were analyzed in the time and frequency domain and using discrete wavelet transformation. Based on the results obtained, sets of indicators were determined, which were used as features for an autonomous classification of the type of defect. Several types of learning algorithms from the group of supervised machine learning were considered in the research. The performance of individual classifiers was determined by several quality evaluation measures. As a result of the analyses, the type and characteristics of the most optimal algorithm to be used in the process of classification of the OLTC fault type were indicated, depending on the type of sensor with which AE signals were recorded. Full article

(This article belongs to the Special Issue Advances in Sensors and Sensing for Technical Condition Assessment and NDT)

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14 pages, 4342 KiB

Open AccessArticle

Ultrasonic Propagation in Highly Attenuating Insulation Materials

by David A. Hutchins, Richard L. Watson, Lee A. J. Davis, Lolu Akanji, Duncan R. Billson, Pietro Burrascano, Stefano Laureti and Marco Ricci

Sensors 2020, 20(8), 2285; https://doi.org/10.3390/s20082285 - 17 Apr 2020

Cited by 15 | Viewed by 3306

Abstract

Experiments have been performed to demonstrate that ultrasound in the 100–400 kHz frequency range can be used to propagate signals through various types of industrial insulation. This is despite the fact that they are highly attenuating to ultrasonic signals due to scattering and viscoelastic effects. The experiments used a combination of piezocomposite transducers and pulse compression processing. This combination allowed signal-to-noise levels to be enhanced so that signals reflected from the surface of an insulated and cladded steel pipe could be obtained. Full article

(This article belongs to the Special Issue Advances in Sensors and Sensing for Technical Condition Assessment and NDT)

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Other

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11 pages, 3108 KiB

Open AccessLetter

Internal Cylinder Identification Based on Different Transmission of Longitudinal and Shear Ultrasonic Waves

by Wen-Bei Liu, Wen-Bo Yan, Huan Liu, Cheng-Guo Tong, Ya-Xian Fan and Zhi-Yong Tao

Sensors 2021, 21(3), 723; https://doi.org/10.3390/s21030723 - 21 Jan 2021

Cited by 4 | Viewed by 1766

Abstract

We have built a Fizeau fiber interferometer to investigate the internal cylindrical defects in an aluminum plate based on laser ultrasonic techniques. The ultrasound is excited in the plate by a Q-switched Nd:YAG laser. When the ultrasonic waves interact with the internal defects, the transmitted amplitudes of longitudinal and shear waves are different. The experimental results show that the difference in transmission amplitudes can be attributed to the high frequency damping of internal cylinders. When the scanning point is close to the internal defect, the longitudinal waves attenuate significantly in the whole defect area, and their amplitude is always smaller than that of shear waves. By comparing the transmitted amplitudes of longitudinal and shear waves at different scanning points, we can achieve a C scan image of the sample to realize the visual inspection of internal defects. Our system exhibits outstanding performance in detecting internal cylinders, which could be used not only in evaluating structure cracks but also in exploring ultrasonic transmission characteristics. Full article

(This article belongs to the Special Issue Advances in Sensors and Sensing for Technical Condition Assessment and NDT)

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10 pages, 1864 KiB

Open AccessLetter

Quality Assessment during Incubation Using Image Processing

by Sheng-Yu Tsai, Cheng-Han Li, Chien-Chung Jeng and Ching-Wei Cheng

Sensors 2020, 20(20), 5951; https://doi.org/10.3390/s20205951 - 21 Oct 2020

Cited by 7 | Viewed by 2848

Abstract

The fertilized egg is an indispensable production platform for making egg-based vaccines. This study was divided into two parts. In the first part, image processing was employed to analyze the absorption spectrum of fertilized eggs; the results show that the 580-nm band had the most significant change. In the second part, a 590-nm-wavelength LED was selected as the light source for the developed detection device. Using this device, sample images (in RGB color space) of the eggs were obtained every day during the experiment. After calculating the grayscale value of the red layer, the receiver operating characteristic curve was used to analyze the daily data to obtain the area under the curve. Subsequently, the best daily grayscale value for classifying unfertilized eggs and dead-in-shell eggs was obtained. Finally, an industrial prototype of the device designed and fabricated in this study was operated and verified. The results show that the accuracy for detecting unfertilized eggs was up to 98% on the seventh day, with the sensitivity and Youden’s index being 82% and 0.813, respectively. On the ninth day, both accuracy and sensitivity reached 100%, and Youden’s index reached a value of 1, showing good classification ability. Considering the industrial operating conditions, this method was demonstrated to be commercially applicable because, when used to detect unfertilized eggs and dead-in-shell eggs on the ninth day, it could achieve accuracy and sensitivity of 100% at the speed of five eggs per second. Full article

(This article belongs to the Special Issue Advances in Sensors and Sensing for Technical Condition Assessment and NDT)

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13 pages, 2728 KiB

Open AccessLetter

New Proposal for Inverse Algorithm Enhancing Noise Robust Eddy-Current Non-Destructive Evaluation

by Milan Smetana, Lukas Behun, Daniela Gombarska and Ladislav Janousek

Sensors 2020, 20(19), 5548; https://doi.org/10.3390/s20195548 - 28 Sep 2020

Cited by 2 | Viewed by 1798

Abstract

Solution of inverse problem in eddy-current non-destructive evaluation of material defects is concerned in this study. A new inverse algorithm incorporating three methods is proposed. The wavelet transform of sensed eddy-current responses complemented by the principal component analysis and followed by the neural network classification are employed for this purpose. The goal is to increase the noise robustness of the evaluation. The proposed inverse algorithm is tested using real eddy-current response data gained from artificial electro-discharge machined notches made in austenitic stainless-steel biomaterial. Eddy-current responses due to the material defects are acquired using a newly developed eddy-current probe that senses separately three spatial components of the perturbed electromagnetic field. The presented results clearly show that the error in evaluation of material defect depth using the proposed algorithm is less than 10% even when the signal-to-noise ratio is as high as 10 dB. Full article

(This article belongs to the Special Issue Advances in Sensors and Sensing for Technical Condition Assessment and NDT)

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15 pages, 8557 KiB

Open AccessLetter

Temperature Field Boundary Conditions and Lateral Temperature Gradient Effect on a PC Box-Girder Bridge Based on Real-Time Solar Radiation and Spatial Temperature Monitoring

by Xiao Lei, Xutao Fan, Hanwan Jiang, Kunning Zhu and Hanyu Zhan

Sensors 2020, 20(18), 5261; https://doi.org/10.3390/s20185261 - 15 Sep 2020

Cited by 22 | Viewed by 2433

Abstract

Climate change could impose great influence on infrastructures. Previous studies have shown that solar radiation is one of the most important factors causing the change in temperature distribution in bridges. The current temperature distribution models developed in the past are mainly based on the meteorological data from the nearest weather station, empirical formulas, or the testing data from model tests. In this study, a five-span continuous Prestressed-concrete box-girder bridge was instrumented with pyranometers, anemometers, strain gauges, displacement gauges, and temperature sensors on the top and bottom slabs and webs to measure the solar radiation, wind speeds, strain, displacement, and surface temperatures, respectively. The continuously monitoring data between May 2019 and May 2020 was used to study the temperature distributions caused by solar radiation. A maximum positive lateral temperature gradient prediction model has been developed based on the solar radiation data analysis. Then, the solar radiation boundary condition obtained from the monitoring data and the lateral temperature gradient prediction model were utilized to compute the tensile stresses in the longitudinal and transverse directions. It was demonstrated in this study that the tensile stress caused by the lateral temperature gradient was so significant that it cannot be ignored in structural design. Full article

(This article belongs to the Special Issue Advances in Sensors and Sensing for Technical Condition Assessment and NDT)

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