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Advanced Sensing and Evaluating Technology in Nondestructive Testing

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 31082

Special Issue Editors

Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Interests: nondestructive testing and evaluation; electromagnetic measurement; intelligent sensors; signal processing; pipeline fault diagnosis
Special Issues, Collections and Topics in MDPI journals
School of Measuring and Optical Engineering, Nanchang Hangkong University, Nanchang 330063, China
Interests: electromagnetic nondestructive testing technology and instruments; eddy current testing; magnetic memory testing
Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Interests: electromagnetic nondestructive testing and evaluation; intelligent sensor; signal processing technology

Special Issue Information

Dear Colleagues,

Nondestructive testing (NDT) is an essential and effective tool for industrial development, which can effectively detect and evaluate the defects of in-service equipment, specimens and materials. At present, NDT technology has been widely used in oil and gas storage and transportation, railway transportation, bridge construction, industrial manufacturing, electric power systems and many other fields, providing necessary testing means and guiding bases for safe operation and maintenance of the key equipment. Advanced NDT results depend on high performance sensing technology and efficient data analysis and evaluation methods. Therefore, the development and application of sensing and evaluating technology has received extensive attention in the field of NDT.This Special Issue aims to present and disseminate the most recent advanced sensing and evaluating technology based on all types of nondestructive testing methods.In this Special Issue, we look forward to receiving papers on a wide range of research topics, not limited to the following topics:

  • Sensors and detection equipment;
  • Application technology of nondestructive testing;
  • Basic theory and simulation modeling of NDT;
  • Industrial control methods and intelligent robots;
  • Signal processing;
  • Defect classification, quantification and imaging;
  • Artificial intelligence applications in NDT;
  • Fault diagnosis and failure analysis;
  • Sensor applications in industrial Internet solutions;
  • Field applications of NDT.

Prof. Dr. Songling Huang
Prof. Dr. Kai Song
Dr. Lisha Peng
Guest Editors

Manuscript Submission Information

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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 testing
  • sensor
  • sensing technology
  • industrial control methods
  • signal processing
  • defect evaluation
  • fault diagnosis
  • artificial intelligence
  • industrial internet solutions
  • NDT methods and applications

Published Papers (23 papers)

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Research

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22 pages, 13293 KiB  
Article
Research on Delamination Damage Quantification Detection of CFRP Bending Plate Based on Lamb Wave Mode Control
by Quanpeng Yu, Shiyuan Zhou, Yuhan Cheng and Yao Deng
Sensors 2024, 24(6), 1790; https://doi.org/10.3390/s24061790 - 10 Mar 2024
Viewed by 499
Abstract
The carbon-fiber-reinforced polymer (CFRP) bending structure is widely used in aviation. The emergence and spread of delamination damage will decrease the safety of in-service bending structures. Lamb waves can effectively identify delamination damage as a high-damage-sensitivity detection tool. For this present study, the [...] Read more.
The carbon-fiber-reinforced polymer (CFRP) bending structure is widely used in aviation. The emergence and spread of delamination damage will decrease the safety of in-service bending structures. Lamb waves can effectively identify delamination damage as a high-damage-sensitivity detection tool. For this present study, the signal difference coefficient (SDC) was introduced to quantify delamination damage and evaluate the sensitivity of A0-mode and S0-mode Lamb waves to delamination damage. The simulation results show that compared with the S0-mode Lamb wave, the A0-mode Lamb wave exhibits higher delamination damage sensitivity. The delamination damage can be quantified based on the strong correlation between the SDC and the delamination damage size. The control effect of the linear array PZT phase time-delay method on the Lamb wave mode was investigated by simulation. The phase time-delay method realizes the generation of a single-mode Lamb wave, which can separately excite the A0-mode and S0-mode Lamb wave to identify delamination damage of different sizes. The A0-mode Lamb wave was excited by the developed one-dimensional miniaturized linear comb transducer (LCT), which was used to conduct the detection experiment on the CFRP bending plate with delamination damage sizes of Φ6.0 mm, Φ10.0 mm, and Φ15.0 mm. The experimental results verify the correctness of the simulation. According to the Hermite interpolation results of the finite-element simulation data, the relationship between the delamination damage size and the SDC was fitted by the Gaussian function and Rational function, which can accurately quantify the delamination damage. The absolute error of the delamination damage quantification with Gaussian and Rational fitting expression does not exceed 0.8 mm and 0.7 mm, and the percentage error is not more than 8% and 7%. The detection and signal processing methods employed in the present research are easy to operate and implement, and accurate delamination damage quantification results have been obtained. Full article
(This article belongs to the Special Issue Advanced Sensing and Evaluating Technology in Nondestructive Testing)
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17 pages, 2348 KiB  
Article
MFGAN: Multimodal Fusion for Industrial Anomaly Detection Using Attention-Based Autoencoder and Generative Adversarial Network
by Xinji Qu, Zhuo Liu, Chase Q. Wu, Aiqin Hou, Xiaoyan Yin and Zhulian Chen
Sensors 2024, 24(2), 637; https://doi.org/10.3390/s24020637 - 19 Jan 2024
Cited by 1 | Viewed by 703
Abstract
Anomaly detection plays a critical role in ensuring safe, smooth, and efficient operation of machinery and equipment in industrial environments. With the wide deployment of multimodal sensors and the rapid development of Internet of Things (IoT), the data generated in modern industrial production [...] Read more.
Anomaly detection plays a critical role in ensuring safe, smooth, and efficient operation of machinery and equipment in industrial environments. With the wide deployment of multimodal sensors and the rapid development of Internet of Things (IoT), the data generated in modern industrial production has become increasingly diverse and complex. However, traditional methods for anomaly detection based on a single data source cannot fully utilize multimodal data to capture anomalies in industrial systems. To address this challenge, we propose a new model for anomaly detection in industrial environments using multimodal temporal data. This model integrates an attention-based autoencoder (AAE) and a generative adversarial network (GAN) to capture and fuse rich information from different data sources. Specifically, the AAE captures time-series dependencies and relevant features in each modality, and the GAN introduces adversarial regularization to enhance the model’s ability to reconstruct normal time-series data. We conduct extensive experiments on real industrial data containing both measurements from a distributed control system (DCS) and acoustic signals, and the results demonstrate the performance superiority of the proposed model over the state-of-the-art TimesNet for anomaly detection, with an improvement of 5.6% in F1 score. Full article
(This article belongs to the Special Issue Advanced Sensing and Evaluating Technology in Nondestructive Testing)
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21 pages, 9339 KiB  
Article
Influence of Tools and Cutting Strategy on Milling Conditions and Quality of Horizontal Thin-Wall Structures of Titanium Alloy Ti6Al4V
by Szymon Kurpiel, Bartosz Cudok, Krzysztof Zagórski, Jacek Cieślik, Krzysztof Skrzypkowski and Witold Brostow
Sensors 2023, 23(24), 9905; https://doi.org/10.3390/s23249905 - 18 Dec 2023
Viewed by 619
Abstract
Titanium and nickel alloys are used in the creation of components exposed to harsh and variable operating conditions. Such components include thin-walled structures with a variety of shapes created using milling. The driving factors behind the use of thin-walled components include the desire [...] Read more.
Titanium and nickel alloys are used in the creation of components exposed to harsh and variable operating conditions. Such components include thin-walled structures with a variety of shapes created using milling. The driving factors behind the use of thin-walled components include the desire to reduce the weight of the structures and reduce the costs, which can sometimes be achieved by reducing the machining time. This situation necessitates, among other things, the use of new machining methods and/or better machining parameters. The available tools, geometrically designed for different strategies, allow working with similar and improved cutting parameters (increased cutting speeds or higher feed rates) without jeopardizing the necessary quality of finished products. This approach causes undesirable phenomena, such as the appearance of vibrations during machining, which adversely affect the surface quality including the surface roughness. A search is underway for cutting parameters that will minimize the vibration while meeting the quality requirements. Therefore, researching and evaluating the impact of cutting conditions are justified and common in scientific studies. In our work, we have focused on the quality characteristics of horizontal thin-walled structures from Ti6Al4V titanium alloys obtained in the milling process. Our experiments were conducted under controlled cutting conditions at a constant value of the material removal rate (2.03 cm3⁄min), while an increased value of the cut layer was used and tested for use in finishing machining. We used three different cutting tools, namely, one for general purpose machining, one for high-performance machining, and one for high-speed machining. Two strategies were adopted: adaptive face milling and adaptive cylindrical milling. The output quantities included the results of acceleration vibration amplitudes, and selected surface topography parameters of waviness (Wa and Wz) and roughness (Ra and Rz). The lowest values of the pertinent quantities were found for a sample machined with a high-performance tool using adaptive face milling. Surfaces typical of chatter vibrations were seen for all samples. Full article
(This article belongs to the Special Issue Advanced Sensing and Evaluating Technology in Nondestructive Testing)
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21 pages, 8700 KiB  
Article
A Hybrid Deep Learning Approach: Integrating Short-Time Fourier Transform and Continuous Wavelet Transform for Improved Pipeline Leak Detection
by Muhammad Farooq Siddique, Zahoor Ahmad, Niamat Ullah and Jongmyon Kim
Sensors 2023, 23(19), 8079; https://doi.org/10.3390/s23198079 - 25 Sep 2023
Cited by 4 | Viewed by 1212
Abstract
A hybrid deep learning approach was designed that combines deep learning with enhanced short-time Fourier transform (STFT) spectrograms and continuous wavelet transform (CWT) scalograms for pipeline leak detection. Such detection plays a crucial role in ensuring the safety and integrity of fluid transportation [...] Read more.
A hybrid deep learning approach was designed that combines deep learning with enhanced short-time Fourier transform (STFT) spectrograms and continuous wavelet transform (CWT) scalograms for pipeline leak detection. Such detection plays a crucial role in ensuring the safety and integrity of fluid transportation systems. The proposed model leverages the power of STFT and CWT to enhance detection capabilities. The pipeline’s acoustic emission signals during normal and leak operating conditions undergo transformation using STFT and CWT, creating scalograms representing energy variations across time–frequency scales. To improve the signal quality and eliminate noise, Sobel and wavelet denoising filters are applied to the scalograms. These filtered scalograms are then fed into convolutional neural networks, extracting informative features that harness the distinct characteristics captured by both STFT and CWT. For enhanced computational efficiency and discriminatory power, principal component analysis is employed to reduce the feature space dimensionality. Subsequently, pipeline leaks are accurately detected and classified by categorizing the reduced dimensional features using t-distributed stochastic neighbor embedding and artificial neural networks. The hybrid approach achieves high accuracy and reliability in leak detection, demonstrating its effectiveness in capturing both spectral and temporal details. This research significantly contributes to pipeline monitoring and maintenance and offers a promising solution for real-time leak detection in diverse industrial applications. Full article
(This article belongs to the Special Issue Advanced Sensing and Evaluating Technology in Nondestructive Testing)
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15 pages, 4655 KiB  
Article
Determining the Elastic Constants of Isotropic Materials by Measuring the Phase Velocities of the A0 and S0 Modes of Lamb Waves
by Olgirdas Tumšys and Liudas Mažeika
Sensors 2023, 23(15), 6678; https://doi.org/10.3390/s23156678 - 26 Jul 2023
Cited by 1 | Viewed by 700
Abstract
In this study, a new method for determining the elastic constants of isotropic plates using Lamb wave fundamental modes is presented. This method solves the inverse problem, where the elastic constants (Young’s modulus and Poisson’s ratio) of the plate were estimated by measuring [...] Read more.
In this study, a new method for determining the elastic constants of isotropic plates using Lamb wave fundamental modes is presented. This method solves the inverse problem, where the elastic constants (Young’s modulus and Poisson’s ratio) of the plate were estimated by measuring the phase velocities of the Lamb wave using the Rayleigh–Lamb equations to find the solution and determining the phase velocities of the A0 and S0 modes using a new method. The suitability of the proposed method for determining the elastic constants was evaluated using simulated and experimental signals propagating on an aluminum plate. The theoretical modeling on the aluminum 7075-T6 plate shows that the proposed method allows the determination of the Poisson ratio with a relative error not exceeding 2% and Young’s modulus with a relative error not exceeding 0.5%. The experimental measurements of an aluminum plate of known thickness (2 mm) and density (2685 kg/m3) confirmed the suitability of the proposed method for the measurements of elastic constants. In the proposed method, the processing of ultrasonic signals can be performed in real-time, and the values of the elastic constants can be obtained immediately after scanning the required distance. Full article
(This article belongs to the Special Issue Advanced Sensing and Evaluating Technology in Nondestructive Testing)
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23 pages, 14783 KiB  
Article
Evaluation of the Vibration Signal during Milling Vertical Thin-Walled Structures from Aerospace Materials
by Szymon Kurpiel, Krzysztof Zagórski, Jacek Cieślik, Krzysztof Skrzypkowski and Witold Brostow
Sensors 2023, 23(14), 6398; https://doi.org/10.3390/s23146398 - 14 Jul 2023
Cited by 4 | Viewed by 855
Abstract
The main functions of thin-walled structures—widely used in several industries—are to reduce the weight of the finished product and to increase the rigidity of the structure. A popular method for machining such components, often with complex shapes, is using milling. However, milling involves [...] Read more.
The main functions of thin-walled structures—widely used in several industries—are to reduce the weight of the finished product and to increase the rigidity of the structure. A popular method for machining such components, often with complex shapes, is using milling. However, milling involves undesirable phenomena. One of them is the occurrence of vibrations caused by the operation of moving parts. Vibrations strongly affect surface quality and also have a significant impact on tool wear. Cutting parameters, machining strategies and tools used in milling constitute some of the factors that influence the occurrence of vibrations. An additional difficulty in milling thin-walled structures is the reduced rigidity of the workpiece—which also affects vibration during machining. We have compared the vibration signal for different approaches to machining thin-walled components with vertical walls made of Ti6Al4V titanium alloy and Inconel 625 nickel alloy. A general-purpose cutting tool for machining any type of material was used along with tools for high-performance machining and high-speed machining adapted for titanium and nickel alloys. A comparison of results was made for a constant material removal rate. The Short-Time Fourier Transform (STFT) method provided the acceleration vibration spectrograms for individual samples. Full article
(This article belongs to the Special Issue Advanced Sensing and Evaluating Technology in Nondestructive Testing)
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16 pages, 5667 KiB  
Article
Crystal Analyzer Based Multispectral Microtomography Using CCD-Sensor
by Maxim Grigoriev, Denis Zolotov, Anastasia Ingacheva, Alexey Buzmakov, Irina Dyachkova, Victor Asadchikov and Marina Chukalina
Sensors 2023, 23(14), 6389; https://doi.org/10.3390/s23146389 - 14 Jul 2023
Viewed by 756
Abstract
To solve the problems of spectral tomography, an X-ray optical scheme was proposed, using a crystal analyzer in Laue geometry between the sample and the detector, which allowed for the selection of predetermined pairs of wavelengths from the incident polychromatic radiation to obtain [...] Read more.
To solve the problems of spectral tomography, an X-ray optical scheme was proposed, using a crystal analyzer in Laue geometry between the sample and the detector, which allowed for the selection of predetermined pairs of wavelengths from the incident polychromatic radiation to obtain projection images. On a laboratory X-ray microtomography setup, an experiment was carried out for the first time where a mixture of micro-granules of sodium chloride NaCl, silver behenate AgC22H43O2, and lithium niobate LiNbO3 was used as a test sample to identify their spatial arrangement. The elements were chosen based on the presence of absorption edges in two of the elements in the energy range of the polychromatic spectrum of the probing radiation. The method of projection distortion correction was used to preprocess the obtained projections. To interpret the obtained reconstruction results, the segmentation method based on the analysis of joint histograms was used. This allowed us to identify each of the three substances. To compare the results obtained, additional “reference” tomographic measurements were performed: one in polychromatic and two in monochromatic (MoKα-, MoKβ-lines) modes. It took three times less time for the tomographic experiment with the crystal analyzer, while the reconstruction accuracy was comparable to that of the “reference” tomography. Full article
(This article belongs to the Special Issue Advanced Sensing and Evaluating Technology in Nondestructive Testing)
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18 pages, 5083 KiB  
Article
Centrifugal Pump Fault Diagnosis Based on a Novel SobelEdge Scalogram and CNN
by Wasim Zaman, Zahoor Ahmad, Muhammad Farooq Siddique, Niamat Ullah and Jong-Myon Kim
Sensors 2023, 23(11), 5255; https://doi.org/10.3390/s23115255 - 01 Jun 2023
Cited by 10 | Viewed by 1610
Abstract
This paper presents a novel framework for classifying ongoing conditions in centrifugal pumps based on signal processing and deep learning techniques. First, vibration signals are acquired from the centrifugal pump. The acquired vibration signals are heavily affected by macrostructural vibration noise. To overcome [...] Read more.
This paper presents a novel framework for classifying ongoing conditions in centrifugal pumps based on signal processing and deep learning techniques. First, vibration signals are acquired from the centrifugal pump. The acquired vibration signals are heavily affected by macrostructural vibration noise. To overcome the influence of noise, pre-processing techniques are employed on the vibration signal, and a fault-specific frequency band is chosen. The Stockwell transform (S-transform) is then applied to this band, yielding S-transform scalograms that depict energy fluctuations across different frequencies and time scales, represented by color intensity variations. Nevertheless, the accuracy of these scalograms can be compromised by the presence of interference noise. To address this concern, an additional step involving the Sobel filter is applied to the S-transform scalograms, resulting in the generation of novel SobelEdge scalograms. These SobelEdge scalograms aim to enhance the clarity and discriminative features of fault-related information while minimizing the impact of interference noise. The novel scalograms heighten energy variation in the S-transform scalograms by detecting the edges where color intensities change. These new scalograms are then provided to a convolutional neural network (CNN) for the fault classification of centrifugal pumps. The centrifugal pump fault classification capability of the proposed method outperformed state-of-the-art reference methods. Full article
(This article belongs to the Special Issue Advanced Sensing and Evaluating Technology in Nondestructive Testing)
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22 pages, 8314 KiB  
Article
A Bulk Acoustic Wave Strain Sensor for Near-Field Passive Wireless Sensing
by Xiyue Zou, Li Wen and Bin Hu
Sensors 2023, 23(8), 3904; https://doi.org/10.3390/s23083904 - 12 Apr 2023
Cited by 1 | Viewed by 1498
Abstract
Near-field passive wireless sensors can realize non-contact strain measurement, so these sensors have extensive applications in structural health monitoring. However, these sensors suffer from low stability and short wireless sensing distance. This paper presents a bulk acoustic wave (BAW) passive wireless strain sensor, [...] Read more.
Near-field passive wireless sensors can realize non-contact strain measurement, so these sensors have extensive applications in structural health monitoring. However, these sensors suffer from low stability and short wireless sensing distance. This paper presents a bulk acoustic wave (BAW) passive wireless strain sensor, which consists of two coils and a BAW sensor. The force-sensitive element is a quartz wafer with a high quality factor, which is embedded into the sensor housing, so the sensor can convert the strain of the measured surface into the shift of resonant frequency. A double-mass-spring-damper model is developed to analyze the interaction between the quartz and the sensor housing. A lumped parameter model is established to investigate the influence of the contact force on the sensor signal. Experiments show that a prototype BAW passive wireless sensor has a sensitivity of 4 Hz/με when the wireless sensing distance is 10 cm. The resonant frequency of the sensor is almost independent of the coupling coefficient, which indicates that the sensor can reduce the measurement error caused by misalignment or relative movement between coils. Thanks to the high stability and modest sensing distance, this sensor may be compatible with a UAV-based monitoring platform for the strain monitoring of large buildings. Full article
(This article belongs to the Special Issue Advanced Sensing and Evaluating Technology in Nondestructive Testing)
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21 pages, 3366 KiB  
Article
Wire Rope Defect Recognition Method Based on MFL Signal Analysis and 1D-CNNs
by Shiwei Liu and Muchao Chen
Sensors 2023, 23(7), 3366; https://doi.org/10.3390/s23073366 - 23 Mar 2023
Cited by 5 | Viewed by 1959
Abstract
The quantitative defect detection of wire rope is crucial to guarantee safety in various application scenes, and sophisticated inspection conditions usually lead to the accurate testing of difficulties and challenges. Thus, a magnetic flux leakage (MFL) signal analysis and convolutional neural networks (CNNs)-based [...] Read more.
The quantitative defect detection of wire rope is crucial to guarantee safety in various application scenes, and sophisticated inspection conditions usually lead to the accurate testing of difficulties and challenges. Thus, a magnetic flux leakage (MFL) signal analysis and convolutional neural networks (CNNs)-based wire rope defect recognition method was proposed to solve this challenge. Typical wire rope defect inspection data obtained from one-dimensional (1D) MFL testing were first analyzed both in time and frequency domains. After the signal denoising through a new combination of Haar wavelet transform and differentiated operation and signal preprocessing by normalization, ten main features were used in the datasets, and then the principles of the proposed MFL and 1D-CNNs-based wire rope defect classifications were presented. Finally, the performance of the novel method was evaluated and compared with six machine learning methods and related algorithms, which demonstrated that the proposed method featured the highest testing accuracy (>98%) and was valid and feasible for the quantitative and accurate detection of broken wire defects. Additionally, the considerable application potential as well as the limitations of the proposed methods, and future work, were discussed. Full article
(This article belongs to the Special Issue Advanced Sensing and Evaluating Technology in Nondestructive Testing)
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16 pages, 13582 KiB  
Article
Non-Destructive Inspection of High Temperature Piping Combining Ultrasound and Eddy Current Testing
by David Santos, Miguel A. Machado, João Monteiro, José P. Sousa, Carla S. Proença, Fernando S. Crivellaro, Luís S. Rosado and Telmo G. Santos
Sensors 2023, 23(6), 3348; https://doi.org/10.3390/s23063348 - 22 Mar 2023
Cited by 5 | Viewed by 2377
Abstract
This paper presents an automated Non-Destructive Testing (NDT) system for the in-service inspection of orbital welds on tubular components operating at temperatures as high as 200 °C. The combination of two different NDT methods and respective inspection systems is here proposed to cover [...] Read more.
This paper presents an automated Non-Destructive Testing (NDT) system for the in-service inspection of orbital welds on tubular components operating at temperatures as high as 200 °C. The combination of two different NDT methods and respective inspection systems is here proposed to cover the detection of all potential defective weld conditions. The proposed NDT system combines ultrasounds and Eddy current techniques with dedicated approaches for dealing with high temperature conditions. Phased array ultrasound was employed, searching for volumetric defects within the weld bead volume while Eddy currents were used to look for surface and sub-surface cracks. The results from the phased array ultrasound results showed the effectiveness of the cooling mechanisms and that temperature effects on sound attenuation can be easily compensated for up to 200 °C. The Eddy current results showed almost no influence when temperatures were raised up to 300 °C. Full article
(This article belongs to the Special Issue Advanced Sensing and Evaluating Technology in Nondestructive Testing)
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12 pages, 9899 KiB  
Communication
A Novel Dual-Permanent-Magnet Mechanical Antenna for Pipeline Robot Localization and Communication
by Yahao Dong, Jing Wu, Xinran Zhang and Tianyu Xie
Sensors 2023, 23(6), 3228; https://doi.org/10.3390/s23063228 - 17 Mar 2023
Cited by 1 | Viewed by 1349
Abstract
The demand for pipeline inspection has promoted the development of pipeline robots and associated localization and communication technologies. Among these technologies, ultra-low-frequency (30–300 Hz) electromagnetic waves have a significant advantage because of their strong penetration, which can penetrate metal pipe walls. Traditional low-frequency [...] Read more.
The demand for pipeline inspection has promoted the development of pipeline robots and associated localization and communication technologies. Among these technologies, ultra-low-frequency (30–300 Hz) electromagnetic waves have a significant advantage because of their strong penetration, which can penetrate metal pipe walls. Traditional low-frequency transmitting systems are limited by the size and power consumption of the antennas. In this work, a new type of mechanical antenna based on dual permanent magnets was designed to solve the above problems. An innovative amplitude modulation scheme that involves changing the magnetization angle of dual permanent magnets is proposed. The ultra-low-frequency electromagnetic wave emitted by the mechanical antenna inside the pipeline can be easily received by the antenna outside to localize and communicate with the robots inside. The experimental results showed that when two N38M-type Nd–Fe–B permanent magnets with a volume of 3.93 cm3 each were used, the magnetic flux density reached 2.35 nT at 10 m in the air and the amplitude modulation performance was satisfactory. Additionally, the electromagnetic wave was effectively received at 3 m from the 20# steel pipeline, which preliminarily verified the feasibility of using the dual-permanent-magnet mechanical antenna to achieve localization of and communication with pipeline robots. Full article
(This article belongs to the Special Issue Advanced Sensing and Evaluating Technology in Nondestructive Testing)
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12 pages, 4425 KiB  
Article
Energy Transfer Efficiency Based Nonlinear Ultrasonic Testing Technique for Debonding Defects of Aluminum Alloy Foam Sandwich Panels
by Jun Tu, Nan Yao, Yi Ling, Xu Zhang and Xiaochun Song
Sensors 2023, 23(6), 3008; https://doi.org/10.3390/s23063008 - 10 Mar 2023
Viewed by 905
Abstract
In order to improve the accuracy of detection results of debonding defects of aluminum alloy thin plate, the nonlinear ultrasonic technology is used to detect the simulated defect samples, aiming at problems such as near surface blind region caused by the interaction of [...] Read more.
In order to improve the accuracy of detection results of debonding defects of aluminum alloy thin plate, the nonlinear ultrasonic technology is used to detect the simulated defect samples, aiming at problems such as near surface blind region caused by the interaction of incident wave, reflected wave and even second harmonic wave in a short time due to the small thickness of thin plates. An integral method based on energy transfer efficiency is proposed to calculate the nonlinear ultrasonic coefficient to characterize the debonding defects of thin plates. A series of simulated debonding defects of different sizes were made using aluminum alloy plates with four thicknesses of 1 mm, 2 mm, 3 mm and 10 mm. By comparing the traditional nonlinear coefficient with the integral nonlinear coefficient proposed in this paper, it is verified that both methods can quantitatively characterize the size of debonding defects. The nonlinear ultrasonic testing technology based on energy transfer efficiency has higher testing accuracy for thin plates. Full article
(This article belongs to the Special Issue Advanced Sensing and Evaluating Technology in Nondestructive Testing)
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18 pages, 6008 KiB  
Article
Quantitative Detection of Tank Floor Defects by Pseudo-Color Imaging of Three-Dimensional Magnetic Flux Leakage Signals
by Zhijun Yang, Jiang Yang, Huaiqing Cao, Han Sun, Yazhong Zhao, Bowen Zhang and Changpeng Meng
Sensors 2023, 23(5), 2691; https://doi.org/10.3390/s23052691 - 01 Mar 2023
Viewed by 1063
Abstract
Highly integrated three-dimensional magnetic sensors have just been developed and have been used in some fields, such as angle measurement of moving objects. The sensor used in this paper is a three-dimensional magnetic sensor with three Hall probes highly integrated inside; 15 sensors [...] Read more.
Highly integrated three-dimensional magnetic sensors have just been developed and have been used in some fields, such as angle measurement of moving objects. The sensor used in this paper is a three-dimensional magnetic sensor with three Hall probes highly integrated inside; 15 sensors are used to design the sensor array and then measure the magnetic field leakage of the steel plate; the three-dimensional component characteristics of the magnetic field leakage are used to determine the defect area. Pseudo-color imaging is the most widely used in the imaging field. In this paper, color imaging is used to process magnetic field data. Compared with analyzing the three-dimensional magnetic field information obtained directly, this paper converts the magnetic field information into color image information through pseudo-color imaging and then obtains the color moment characteristic values of the color image in the defect area. Moreover, the least-square support-vector machine and particle swarm optimization (PSO-LSSVM) algorithm are used to quantitatively identify the defects. The results show that the three-dimensional component of the magnetic field leakage can effectively determine the area range of defects, and it is feasible to use the color image characteristic value of the three-dimensional magnetic field leakage signal to identify defects quantitatively. Compared with a single component, the three-dimensional component can effectively improve the identification rate of defects. Full article
(This article belongs to the Special Issue Advanced Sensing and Evaluating Technology in Nondestructive Testing)
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23 pages, 7882 KiB  
Article
Application of Pulse Compression Technique in High-Temperature Carbon Steel Forgings Crack Detection with Angled SV-Wave EMATs
by Min He, Wenze Shi, Chao Lu, Guo Chen, Fasheng Qiu, Ying Zhu and Yuan Liu
Sensors 2023, 23(5), 2685; https://doi.org/10.3390/s23052685 - 01 Mar 2023
Cited by 1 | Viewed by 1415
Abstract
In order to solve the difficulty in localization and poor signal-to-noise ratio (SNR) of the angled shear vertical wave (SV wave) electromagnetic acoustic transducer (EMAT) in cracks detection of high-temperature carbon steel forgings, a finite element (FE) model of the angled SV wave [...] Read more.
In order to solve the difficulty in localization and poor signal-to-noise ratio (SNR) of the angled shear vertical wave (SV wave) electromagnetic acoustic transducer (EMAT) in cracks detection of high-temperature carbon steel forgings, a finite element (FE) model of the angled SV wave EMAT detection process was established, and the influence of specimen temperature on the EMAT excitation, propagation, and reception processes was analyzed. A high-temperature resistant angled SV wave EMAT was designed to detect carbon steel from 20 °C to 500 °C, and the influence law of the angled SV wave at different temperatures was analyzed. Then a circuit-field coupled FE model of angled SV wave EMAT in the carbon steel detection process based on the Barker code pulse compression technique was established, and the effects of the Barker code element length, impedance matching method, and matching component parameters on the pulse compression effect were analyzed. In addition, the noise suppression effect and the SNR of the crack-reflected wave in the tone-burst excitation method and the Barker code pulse compression technique were compared. The results show that the amplitude of the block-corner reflected wave decreases from 556 mV to 195 mV, and the SNR decreases from 34.9 dB to 23.5 dB when the specimen temperature increases from 20 °C to 500 °C. When the temperature is 500 °C, the SNR of the crack-reflected wave obtained by the Barker code pulse compression technique can be improved by 9.2 dB compared to the tone-burst excitation method with 16 synchronous averages. The study can provide technical and theoretical guidance for online crack detection for high-temperature carbon steel forgings. Full article
(This article belongs to the Special Issue Advanced Sensing and Evaluating Technology in Nondestructive Testing)
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16 pages, 4211 KiB  
Article
Micromagnetic and Robust Evaluation of Surface Hardness in Cr12MoV Steel Considering Repeatability of the Instrument
by Zhixiang Xing, Xianxian Wang, Mengshuai Ning, Cunfu He and Xiucheng Liu
Sensors 2023, 23(3), 1273; https://doi.org/10.3390/s23031273 - 22 Jan 2023
Cited by 2 | Viewed by 1101
Abstract
The combination of multifunctional micromagnetic testing and neural network-based prediction models is a promising way of nondestructive and quantitative measurement of steel surface hardness. Current studies mainly focused on improving the prediction accuracy of intelligent models, but the unavoidable and random uncertainties related [...] Read more.
The combination of multifunctional micromagnetic testing and neural network-based prediction models is a promising way of nondestructive and quantitative measurement of steel surface hardness. Current studies mainly focused on improving the prediction accuracy of intelligent models, but the unavoidable and random uncertainties related to instruments were seldom explored. The robustness of the prediction model considering the repeatability of instruments was seldom discussed. In this work, a self-developed multifunctional micromagnetic instrument was employed to perform the repeatability test with Cr12MoV steel. The repeatability of the instrument in measuring multiple magnetic features under both static and dynamic conditions was evaluated. The magnetic features for establishing the prediction model were selected based on the consideration of both the repeatability of the instrument and the ability of magnetic features in surface hardness evaluation. To improve the robustness of the model in surface hardness prediction, a modelling strategy considering the repeatability of the instrument was proposed. Through removing partial magnetic features with higher mean impact values from input nodes, robust evaluation of surface hardness in Cr12MoV steel was realized with the multifunctional micromagnetic instrument. Full article
(This article belongs to the Special Issue Advanced Sensing and Evaluating Technology in Nondestructive Testing)
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16 pages, 5534 KiB  
Article
Time-Domain Numerical Simulation and Experimental Study on Pulsed Eddy Current Inspection of Tubing and Casing
by Xingxing Yu, Ying Zhu, Yan Cao and Juan Xiong
Sensors 2023, 23(3), 1135; https://doi.org/10.3390/s23031135 - 18 Jan 2023
Cited by 1 | Viewed by 1343
Abstract
Fundamental theory and methods are investigated of inspecting tubing and casing simultaneously using pulsed eddy current testing by numerical simulations and experiments. The distribution and variation of eddy current field are given in the finite element simulation for the inspection of undamaged and [...] Read more.
Fundamental theory and methods are investigated of inspecting tubing and casing simultaneously using pulsed eddy current testing by numerical simulations and experiments. The distribution and variation of eddy current field are given in the finite element simulation for the inspection of undamaged and corroded casing and tubing combinations, with tubing outer diameter 73.8 mm, wall thickness 5.7 mm, corrosion depth 1.25 mm, 2.5 mm, 3.75 mm, and casing outer diameter 141.5 mm, wall thickness 7.7 mm, corrosion depth 1.25 mm, 2.5 mm, and 3.75 mm, respectively. The results show that eddy current field propagates around and to the depth after the direct section of the exciting current is cut off and the intensity center of eddy current field shifts gradually from the inner side of the tubing to the casing, which forms the basis of analyzing inspection mechanism. Corrosion at a particular depth is related to a particular optimum time slice of the induced voltage (namely with deepest concave) and a highest sensitivity is obtained at this slice. The time associated with this slice is in accordance with the time when the intensity center of eddy current reaches the corrosion. Corrosion at different depths has different voltage time slices starting to show signal of defect, which can be used to estimate the depth of the defect in order to judge the defect coming from tubing or casing. Furthermore, sinking degree of the time slice reflects the size of the defect. All machined defects can be recognized in the experiments and the optimum time slice appears at 0.01 s and 0.008 s after the excitation current is cut off for the tubing corrosion of 1.25 mm and 2.5 mm, respectively. The optimum time slice appears at the last moment of cut-off period, 0.625, for the casing corrosion. Experimental results agree well with the simulations and show the existence of the optimum correspondence between depth of corrosion and starting time of the defect signal of time slice, relations between sinking degree of the time slice, and corrosion size. Full article
(This article belongs to the Special Issue Advanced Sensing and Evaluating Technology in Nondestructive Testing)
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11 pages, 2976 KiB  
Article
Quantitative Identification Method for Glass Panel Defects Using Microwave Detection Based on the CSAPSO-BP Neural Network
by Jun Fang, Zhiyang Deng, Jun Tu and Xiaochun Song
Sensors 2023, 23(3), 1097; https://doi.org/10.3390/s23031097 - 18 Jan 2023
Cited by 1 | Viewed by 1227
Abstract
To address the problem of the quantitative identification of glass panel surface defects, a new method combining the chaotic simulated annealing particle swarm algorithm (CSAPSO) and the BP neural network is proposed for the quantitative evaluation of microwave detection signals of glass panel [...] Read more.
To address the problem of the quantitative identification of glass panel surface defects, a new method combining the chaotic simulated annealing particle swarm algorithm (CSAPSO) and the BP neural network is proposed for the quantitative evaluation of microwave detection signals of glass panel defects. First, the parameters of the particle swarm optimization (PSO) algorithm are dynamically assigned using chaos theory to improve the global search capability of the PSO. Then, the CSAPSO-BP neural network model is constructed, and the return loss and phase of the microwave detection echo signal of glass panel defects are extracted as the input feature quantity of the network, from which the intrinsic connection between input and output is found through network training and testing to achieve the prediction of the depth and width of glass panel surface defects. The results show that the CSAPSO-BP network model can more accurately characterize the defect geometry of glass panels than the PSO-BP network model. Full article
(This article belongs to the Special Issue Advanced Sensing and Evaluating Technology in Nondestructive Testing)
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14 pages, 4736 KiB  
Article
Secondary Interface Echoes Suppression for Immersion Ultrasonic Imaging Based on Phase Circular Statistics Vector
by Ming Chen, Zhenghui Xiong, Yan Jing, Xi He, Qingru Kong and Yao Chen
Sensors 2023, 23(3), 1081; https://doi.org/10.3390/s23031081 - 17 Jan 2023
Cited by 1 | Viewed by 1062
Abstract
Immersion ultrasonic phased array imaging technology offers great advantages, particularly in coupling and automatic detection of industrial non-destructive testing (NDT). To suppress the influence of secondary interface echoes in the immersion ultrasonic phased array imaging, a novel phase circular statistics vector (PCSV) weighting [...] Read more.
Immersion ultrasonic phased array imaging technology offers great advantages, particularly in coupling and automatic detection of industrial non-destructive testing (NDT). To suppress the influence of secondary interface echoes in the immersion ultrasonic phased array imaging, a novel phase circular statistics vector (PCSV) weighting method is proposed in this paper. Firstly, the PCSV factor matrix is established according to the phase consistency of the echo signals. Secondly, due to the higher phase coherence of the defect echo, the PCSV factor matrix is used to weight the TFM image to suppress the secondary interface echo. The result shows the secondary interface echoes are effectively suppressed in the total focusing method (TFM) image on a 0~40 dB scale. It is also shown that PCSV weighting could not only suppress the secondary interface echoes but also improved the image quality in terms of SNR and lateral resolution by comparing with traditional TFM. Full article
(This article belongs to the Special Issue Advanced Sensing and Evaluating Technology in Nondestructive Testing)
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12 pages, 4526 KiB  
Article
Solar Power Prediction Using Dual Stream CNN-LSTM Architecture
by Hamad Alharkan, Shabana Habib and Muhammad Islam
Sensors 2023, 23(2), 945; https://doi.org/10.3390/s23020945 - 13 Jan 2023
Cited by 9 | Viewed by 2813
Abstract
The integration of solar energy with a power system brings great economic and environmental benefits. However, the high penetration of solar power is challenging due to the operation and planning of the existing power system owing to the intermittence and randomicity of solar [...] Read more.
The integration of solar energy with a power system brings great economic and environmental benefits. However, the high penetration of solar power is challenging due to the operation and planning of the existing power system owing to the intermittence and randomicity of solar power generation. Achieving accurate predictions for power generation is important to provide high-quality electric energy for end-users. Therefore, in this paper, we introduce a deep learning-based dual-stream convolutional neural network (CNN) and long short-term nemory (LSTM) network followed by a self-attention mechanism network (DSCLANet). Here, CNN is used to learn spatial patterns and LSTM is incorporated for temporal feature extraction. The output spatial and temporal feature vectors are then fused, followed by a self-attention mechanism to select optimal features for further processing. Finally, fully connected layers are incorporated for short-term solar power prediction. The performance of DSCLANet is evaluated on DKASC Alice Spring solar datasets, and it reduces the error rate up to 0.0136 MSE, 0.0304 MAE, and 0.0458 RMSE compared to recent state-of-the-art methods. Full article
(This article belongs to the Special Issue Advanced Sensing and Evaluating Technology in Nondestructive Testing)
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12 pages, 7926 KiB  
Article
Surface Decarburization Depth Detection in Rods of 60Si2Mn Steel with Magnetic Barkhausen Noise Technique
by Peng Li, Xianxian Wang, Dongdong Ding, Zhaoxiang Gao, Wen Fang, Chaolei Zhang, Cunfu He and Xiucheng Liu
Sensors 2023, 23(1), 503; https://doi.org/10.3390/s23010503 - 02 Jan 2023
Cited by 1 | Viewed by 1328
Abstract
Magnetic Barkhausen noise (MBN), sensitive to the microstructure of materials, can be applied in the surface decarburization depth detection of ferromagnetic specimens. However, the effects of core microstructures on the determination results of decarburization depth have not been explored. In this study, MBN [...] Read more.
Magnetic Barkhausen noise (MBN), sensitive to the microstructure of materials, can be applied in the surface decarburization depth detection of ferromagnetic specimens. However, the effects of core microstructures on the determination results of decarburization depth have not been explored. In this study, MBN was employed to evaluate the magnetic properties of the decarburized 60Si2Mn spring steels with martensitic and pearlitic core microstructures. Spring steel samples were austenitized at different times to generate different decarburization depths. Seven magnetic features were extracted from the MBN butterfly profiles. We used the variation coefficient, linear correlation coefficient, and normalized sensitivity to discuss the influence of the core microstructures on these seven features. The different core microstructures led to a large difference in the ability of MBN features to characterize the decarburization layer depth. However, three features of MBN butterfly profiles demonstrated an approximately linear dependency (linear correlation coefficient > 94%) on surface decarburization depth and monotonically increased with the increase in depth in both core microstructures of spring steels. Full article
(This article belongs to the Special Issue Advanced Sensing and Evaluating Technology in Nondestructive Testing)
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14 pages, 3923 KiB  
Article
PCB-Based Planar Inductive Loops for Partial Discharges Detection in Power Cables
by Sinda Kaziz, Pietro Romano, Antonino Imburgia, Guido Ala, Halim Sghaier, Denis Flandre and Fares Tounsi
Sensors 2023, 23(1), 290; https://doi.org/10.3390/s23010290 - 27 Dec 2022
Cited by 2 | Viewed by 2061
Abstract
Partial discharge (PD) diagnosis tests, including detecting, locating, and identifying, are used to trace defects or faults and assess the degree of aging in order to monitor the insulation condition of medium- and high-voltage power cables. In this context, an experimental evaluation of [...] Read more.
Partial discharge (PD) diagnosis tests, including detecting, locating, and identifying, are used to trace defects or faults and assess the degree of aging in order to monitor the insulation condition of medium- and high-voltage power cables. In this context, an experimental evaluation of three different printed circuit board (PCB)-based inductive sensor topologies, with spiral, non-spiral, and meander shapes, is performed. The aim is to assess their capabilities for PD detection along a transmission power cable. First, simulation and experimental characterization are carried out to determine the equivalent electrical circuit and the quality factor of the three sensors. PD activity was studied in the lab on a 10-m-long defective MVAC cable. The three PCB-based sensors were tested in three different positions: directly on the defective cable (P1), at a separation distance of 10 cm to 3 m (P2), and on the ground line (P3). For the three positions, all sensors’ outputs present a damped sine wave signal with similar frequencies and durations. Experimental results showed that the best sensitivity was given by the non-spiral inductor, with a peak voltage of around 500 mV in P1, 428 mV in P2, and 45 mV in P3, while the meander sensor had the lowest values, which were approximately 80 mV in P1. The frequency spectrum bandwidth of all sensors was between 10 MHz and 45 MHz. The high sensitivity of the non-spiral inductor could be associated with its interesting properties in terms of quality factor and SFR, which are due to its very low resistivity. To benchmark the performance of the designed three-loop sensors, a comparison with a commercial high-frequency current transformer (HFCT) is also made. Full article
(This article belongs to the Special Issue Advanced Sensing and Evaluating Technology in Nondestructive Testing)
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Review

Jump to: Research

29 pages, 2255 KiB  
Review
A Review on Concrete Structural Properties and Damage Evolution Monitoring Techniques
by Jinghua Zhang, Lisha Peng, Shuzhi Wen and Songling Huang
Sensors 2024, 24(2), 620; https://doi.org/10.3390/s24020620 - 18 Jan 2024
Viewed by 696
Abstract
Concrete structures have emerged as some of the most extensively utilized materials in the construction industry due to their inherent plasticity and high-strength characteristics. However, due to the temperature fluctuations, humidity, and damage caused by human activities, challenges such as crack propagation and [...] Read more.
Concrete structures have emerged as some of the most extensively utilized materials in the construction industry due to their inherent plasticity and high-strength characteristics. However, due to the temperature fluctuations, humidity, and damage caused by human activities, challenges such as crack propagation and structural failures pose threats to the safety of people’s lives and property. Meanwhile, conventional non-destructive testing methods are limited to defect detection and lack the capability to provide real-time monitoring and evaluating of concrete structural stability. Consequently, there is a growing emphasis on the development of effective techniques for monitoring the health of concrete structures, facilitating prompt repairs and mitigation of potential instabilities. This paper comprehensively presents traditional and novel methods for concrete structural properties and damage evolution monitoring, including emission techniques, electrical resistivity monitoring, electromagnetic radiation method, piezoelectric transducers, ultrasonic techniques, and the infrared thermography approach. Moreover, the fundamental principles, advantages, limitations, similarities and differences of each monitoring technique are extensively discussed, along with future research directions. Each method has its suitable monitoring scenarios, and in practical applications, several methods are often combined to achieve better monitoring results. The outcomes of this research provide valuable technical insights for future studies and advancements in the field of concrete structural health monitoring. Full article
(This article belongs to the Special Issue Advanced Sensing and Evaluating Technology in Nondestructive Testing)
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