Showing papers by "Gui Yun Tian published in 2018"

Chipless RFID Sensor Tag for Metal Crack Detection and Characterization

Abstract: Chipped radio-frequency identification (RFID) sensor systems have been studied for structural health monitoring (SHM) applications. However, the use of chip in sensor tags and its standardized narrowband operation contribute shortcomings in cost, durability, and detection capability. This paper presents a novel use of the frequency signature-based chipless RFID for metal crack detection and characterization operating in ultra-wideband frequency. The vision is to implement a low-cost and high-temperature-resistant passive wireless sensor able to monitor the crack on a metallic structure with multiparameter detection. We propose a chipless RFID sensor tag integrating four tip-loaded dipole resonators as a 4-bit ID encoder and a circular microstrip patch antenna (CMPA) resonator as a crack sensor. The radar cross section spectrum of the chipless RFID sensor tag generates four resonant frequencies from the dipole resonators and a resonant frequency from the CMPA resonator. Simulation and experimental results show that the resonant frequency shift of the CMPA is a useful feature to indicate the crack orientation and the crack width on a metallic structure. The direction of the resonant frequency shift represents the orientation of the crack, while the magnitude of the resonant frequency shift is proportional to the width of the crack. Furthermore, the experimentation with a natural fatigue crack sample proves that the proposed sensor tag is capable of detecting submillimeter cracks.

Image processing based quantitative damage evaluation in composites with long pulse thermography

Abstract: Pulsed thermography is a contactless and rapid non-destructive evaluation (NDE) technique that is widely used for the inspection of fibre reinforced plastic composites. However, pulsed thermography uses expensive and specialist equipment such high-energy flash lamps to generate heat into the sample, so that alternative thermal stimulation sources are needed. Long pulse thermography was recently developed as a cost-effective solution to enhance the defect detectability in composites by generating step-pulse heat into the test sample with inexpensive quartz halogen lamps and measuring the thermal response during the material cooling down. This paper provides a quantitative comparison of long pulse thermography with traditional pulsed thermography and step heating thermography in carbon fibre and glass fibre composites with flat-bottomed holes located at various depths. The three thermographic methods are processed with advanced thermal image algorithms such as absolute thermal contrast, thermographic signal reconstruction, phase Fourier analysis and principal component analysis in order to reduce thermal image artefacts. Experimental tests have shown that principal component analysis applied to long pulse thermography provides accurate imaging results over traditional pulsed thermography and step heating thermography. Hence, this inspection technique can be considered as an efficient and cost-effective thermographic method for low thermal conductivity and low thermal response rate materials.

Variational Bayesian Subgroup Adaptive Sparse Component Extraction for Diagnostic Imaging System

Abstract: A novel unsupervised sparse component extraction algorithm is proposed for detecting micro defects while employing a thermography imaging system The proposed approach is developed using the variational Bayesian framework This enables a fully automated determination of the model parameters and bypasses the need for human intervention in manually selecting the appropriate image contrast frames An internal subsparse grouping mechanism and adaptive fine-tuning strategy have been built to control the sparsity of the solution The proposed algorithm is computationally affordable and yields a high-accuracy objective performance Experimental tests on both artificial and natural defects have been conducted to verify the efficacy of the proposed method

Remote Authentication Schemes for Wireless Body Area Networks Based on the Internet of Things

Abstract: Internet of Things (IoT) is a new technology which offers enormous applications that make people’s lives more convenient and enhances cities’ development. In particular, smart healthcare applications in IoT have been receiving increasing attention for industrial and academic research. However, due to the sensitiveness of medical information, security and privacy issues in IoT healthcare systems are very important. Designing an efficient secure scheme with less computation time and energy consumption is a critical challenge in IoT healthcare systems. In this paper, a lightweight online/offline certificateless signature (L-OOCLS) is proposed, then a heterogeneous remote anonymous authentication protocol (HRAAP) is designed to enable remote wireless body area networks (WBANs) users to anonymously enjoy healthcare service based on the IoT applications. The proposed L-OOCLS scheme is proven secure in random oracle model and the proposed HRAAP can resist various types of attacks. Compared with the existing relevant schemes, the proposed HRAAP achieves less computation overhead as well as less power consumption on WBANs client. In addition, to nicely meet the application in the IoT, an application scenario is given.

Thermal Pattern Contrast Diagnostic of Microcracks With Induction Thermography for Aircraft Braking Components

Abstract: Reciprocating impact load leads to plastic deformation on the surface of the kinematic chains in an aircraft brake system. As a result, this causes fatigue and various complex natural damages. Due to the complex surface conditions and the coexistence damages, it is extremely difficult to diagnose microcracks by using conventional thermography inspection methods. In this paper, the thermal pattern contrast method is proposed for weak thermal signal detection using eddy current pulsed thermography. In this process, the extraction and subsequent separation differentiate a maximum of the thermal spatial-transient pattern between defect and nondefect areas. Specifically, a successive optical flow is established to conduct a projection of the thermal diffusion. This directly gains the benefits of capturing the thermal propagation characteristics. It enables us to build the motion context connected between the local and the global thermal spatial pattern. Principal component analysis is constructed to further mine the spatial-transient patterns to enhance the detectability and sensitivity in microcrack detection. Finally, experimental studies have been conducted on an artificial crack in a steel sample and on natural fatigue cracks in aircraft brake components in order to validate the proposed method.

Wireless Passive Ultra High Frequency RFID Antenna Sensor for Surface Crack Monitoring and Quantitative Analysis.

Abstract: An exponential increase in large-scale infrastructure facilitates the development of wireless passive sensors for permanent installation and in-service health monitoring. Due to their wireless, passive and cost-effective characteristics, ultra-high frequency (UHF) radio frequency identification (RFID) tag antenna based sensors are receiving increasing attention for structural health monitoring (SHM). This paper uses a circular patch antenna sensor with an open rectangular window for crack monitoring. The sensing mechanism is quantitatively studied in conjunction with a mode analysis, which can uncover the intrinsic principle for turning an antenna into a crack sensor. The robustness of the feature is examined when the variation of crack position associated with an aluminum sample and the antenna sensor is considered. The experimental results demonstrate a reasonable sensitivity and resolution for crack characterization.

HOOSC: heterogeneous online/offline signcryption for the Internet of Things

Abstract: In order to improve the accessibility of the services provided by a sensor network, wireless sensor networks (WSNs) is integrated to Internet of Things (IoTs). In this case, the security is one of the issues be considered when integrating wireless sensor network to IoTs. In this paper, a heterogeneous secure scheme is proposed to build a secure channel between WSNs and Internet server in the IoTs. To achieve better security with minimum cost in WSNs, certificateless and online/offline technique are used. In addition, to increase the scalability at the Internet server side, public key infrastructure is used. As compared with four existing heterogeneous schemes, the proposed scheme has less energy consumption and computational cost. In addition, two application scenarios that illustrate how the proposed scheme can be applied in the IoTs have been given.

Probability of Detection for Eddy Current Pulsed Thermography of Angular Defect Quantification

Abstract: Eddy current pulsed thermography (ECPT) as one of the emerging nondestructive testing and evaluation (NDT&E) techniques has been used for the evaluation of the integrity of rail tracks, especially for rolling contact fatigue (RCF) detection and crack sizing. This paper proposes a probability of detection (POD) analysis framework for the ECPT system. Specifically, three different features, i.e., max thermal response, first-order differential imaging, and ratio mapping of the first-order differential imaging, were used to quantify the length of the angular slot by linear fitting. Based on the fitting relation between these features and the slot length, POD curves for linear-coil-based ECPT system of angular defect detection were calculated and compared. Results show that the max thermal response feature has the highest repeatability and detectability for shorter slot detection. First-order differential imaging and ratio mapping features are more convincing for longer slot detection.

Pattern Deep Region Learning for Crack Detection in Thermography Diagnosis System

Abstract: Eddy Current Pulsed Thermography is a crucial non-destructive testing technology which has a rapidly increasing range of applications for crack detection on metals. Although the unsupervised learning method has been widely adopted in thermal sequences processing, the research on supervised learning in crack detection remains unexplored. In this paper, we propose an end-to-end pattern, deep region learning structure to achieve precise crack detection and localization. The proposed structure integrates both time and spatial pattern mining for crack information with a deep region convolution neural network. Experiments on both artificial and natural cracks have shown attractive performance and verified the efficacy of the proposed structure.

IQ signal based RFID sensors for defect detection and characterisation

Abstract: Radio frequency identification (RFID) sensor systems have unique advantages of identification, communication and sensing together. Previous researches on RFID based sensing investigate power based features, and face the challenges of low sensitivity and robustness due to environment RF field. In this paper, rather than using received signal strength indicator (RSSI), we present a method using features of transient responses from in-phase quadrature (IQ) signal to overcome the challenges of sensitivity and robustness in ultra-high frequency (UHF) RFID sensor systems. The transient responses of the IQ signal are analysed using skewness feature for different defects. The experimental results show that IQ based skewness features from IQ signal improve sensitivity and robustness for defect characterisation compared with previous RSSI and RCS methods.

Emissivity correction using spectrum correlation of infrared and visible images

Abstract: Thermography is a typical sensing approach used for non-destructive testing and evaluation (NDT&E). However, the varying surface emissivity of an object leads to illusory temperature inhomogeneity which results in influences on defect detection. This paper proposes a new technique to correct the influence of the surface’s varying emissivity of an object in active thermography. Two cameras operating at different spectra are used to capture infrared and visible images simultaneously. Although the physics behind infrared and optical imaging are very different, a close spectrum correlation of two images is identified. An invariant coefficient feature has been estimated for an emissivity correction of infrared images with suggested algorithm. The basic hypothesis is that the reflectance correlation is proposed to predict surface emissivity of an object with respect to wavelength. Experimental validation results show that after correction infrared images are looking like more homogeneous and independent of emissivity. It has been tested for a partially painted steel and rail samples with different known emissivity. Comparative analysis demonstrates its promising capability for accurate mapping of thermal patterns and defect evaluation in thermography NDT&E.

Lift-Off Point of Intersection in Spectral Pulsed Eddy Current Signals for Thickness Measurement

Abstract: Lift-off invariant strategies play an important role in pulsed eddy current (PEC) testing. As an effective signal feature immune to lift-off effect, lift-off point of intersection (LOI) has been applied to determine thickness or evaluate defects. Typically, an LOI feature is extracted from time domain PEC signals. In this article, we observed a novel LOI feature in real and imaginary part of frequency domain PEC responses via simulation and experiment. An analytical model is formulated to investigate the novel LOI feature. It is found that the proposed LOI feature varies with sample thickness increasing, which indicates that the LOI feature from spectral PEC signals could be used for thickness measurement.

Depolarizing Chipless RFID Sensor Tag for Characterization of Metal Cracks Based on Dual Resonance Features

Abstract: In structural health monitoring (SHM) applications, chipless RFID sensor systems are potential for acquiring multiple information of metal defects, such as defect orientation and size, due to its broadband operation. However, the issue of limited readability of chipless RFID tags in the real environment and particularly on a large metallic platform raises the need for applying the cross-polarization reading, and thus depolarizing sensor tags are required. This paper introduces a depolarizing chipless RFID sensor tag design for characterization of metal cracks based on dual resonance features. The proposed chipless RFID sensor tag integrates a modified circular patch antenna with notched edges as the crack sensor and diagonal bent dipoles as the tag ID. The notches on the diagonal edges make the circular patch able to depolarize the incident waves and generates two resonances for sensing purpose. To obtain the frequency signature of the sensor tag, a chipless RFID sensor reader is developed from an ultra-wideband (UWB) radar module applying the short-time Fourier transform (STFT). The simulation and experimental studies show that the dual resonance features generated by the depolarizing circular patch can be used to indicate the crack orientation as well as to characterize the crack width in millimeter. With the sensor’s readability over a large metallic platform and the usability of the dual resonance features in crack characterization, the developed chipless RFID system is potential for real implementations of the internet of things (IoT)-based SHM.

Feature Extraction for Robust Crack Monitoring Using Passive Wireless RFID Antenna Sensors

Abstract: Radio-frequency identification (RFID) tag antenna-based passive wireless sensors are receiving increasing attentions for structural health monitoring in large-scale infrastructure. For permanently installed monitoring, robustness to measurement variation including environmental conditions is a practical issue. This paper retrospects the communication principle of magnetic resonant coupling for low-frequency passive wireless RFID antenna sensors. The influence of communication is uncovered and essentially separated from sensing through two steps: sweep (source) frequency to capture the system behavior, i.e., a resonance frequency range, and multiple feature extraction, fusion, and selection in conjunction with normalization for the selected time-domain feature of peak-to-peak. By this way, the robustness of the low-cost RFID sensing system is enhanced. The proposed method is validated by the case study in open crack detection and characterization under varied measurement conditions.

Multicarrier SAR Image Reconstruction Using Integrated MUSIC-LSE Algorithm

Abstract: In synthetic aperture radar (SAR) applications, high-resolution images and effective estimation processes are vital for the reconstruction of any targets. This can be achieved by using multicarrier waveforms such as orthogonal frequency division multiplexing (OFDM) with the help of appropriate signal processing algorithms. However, the quality of the reconstructed image degrades in low signal-to-noise ratio (SNR) environments during SAR data acquisition. In this paper, an integrated multiple signal classification (MUSIC) assisted least square estimation (LSE) algorithm (MUSIC-LSE) is proposed to enhance the quality of the reconstructed SAR image in a low-SNR environment. Simulation results measured and evaluated the quality of the reconstructed image using three performance indicators of root-mean-square-error, main lobe width and cumulative side lobe levels. These indicators are also used to investigate the effect of OFDM subcarrier selection on the reconstructed image for a different number of subcarriers. Experimental validation of the approach is carried out using two steel pipes to image and detect the curvature of the steel pipes. The results show that the proposed MUSIC-LSE approach produces better-reconstructed images compared with the existing linear frequency modulated (LFM) chirp and OFDM-LSE approaches in low-SNR (−10 dB) environments and enables the radar to distinguish and detect the curvature of the pipes even below the radar range and cross-range resolution.

Steel Corrosion Stages Characterization Using Open-Ended Rectangular Waveguide Probe

Abstract: Safety evaluation of steel structures requires knowledge of corrosion progression stages. The deteriorative stage of corrosion involves multiple parameters, and thus it is difficult to be characterized by the model-based approaches. In this paper, we propose a steel corrosion stages characterization method using microwave open-ended rectangular waveguide (ORWG) probes and a statistical-based principal component analysis (PCA) method. Two ORWG probes operating in successive bands, ranging between 9.5 and 26.5 GHz, are utilized to obtain reflection coefficient spectra from specific sets of corrosion samples; i.e., uncoated corrosion progression, coated corrosion progression, and surface preparation. PCA is applied to extract corrosion progression feature from spectral responses of training samples. The robustness of the PC-based features is analyzed with influences of operating frequency, coating layer, and surface condition. It is found that the corrosion features extracted by the first principal component (PC1) from coated and uncoated corrosion samples are highly correlated to the corrosion progress regardless of probe parameter and coating layer.

Comparative Analysis of In-line Inspection Equipments and Technologies

Abstract: Considering the global aging of pipelines and demand for new ones in more and more hostile environments, in-line inspection (ILI) based on the Non-Destructive Testing(NDT) was essential for pipeline operating company to protect their asset efficiently. This paper reviewed kinds of state of the art of ILI techniques and equipments, including geometry pig (GP), magnetic flux leakage pig (MFL PIG), ultrasonic pig (UT PIG), electromagnetic acoustic pig (EMAT PIG), eddy current pig (EC PIG), integrated function pig and specific function pig. Through the review of kinds of techniques, different approaches were compared, challenges and problems were highlighted and future research and direction was suggested.

Novel Ultrasound System With Intelligent Compensation for High Precision Measurement of Thin Wall Tube

Abstract: Ultrasound is widely used for measuring wall-thickness and diameter of tubes. All tubes are required to conduct the full profile of inspection to guarantee the quality by using an automatic nondestructive testing system. However, most of the current ultrasonic testing works were done under the stationary condition for both specimen and probes with limited detection area. There exist challenges for providing a precisely measurement by approaching an automatic ultrasonic testing with high-speed inspection while it suffers the influence from temperature change of the water, mechanical vibration, and tube deformation. In this paper, the spectral analysis of ultrasonic resonance was applied to measure the wall-thickness and diameter of the tubes. Besides, a novel intelligent compensation ultrasonic system with embedded strategy of self-organizing feature mapping artificial neural network is proposed to eliminate the interference under the condition of high-speed inspection. The experimental and comparison studies have been carried out. The corresponding results illustrate that the measurement precision of diameter and wall thickness can be effectively improved by using the proposed method.

Study of Rotating Magnet Array-Based Motion-Induced Eddy Current Thermography

Abstract: To achieve high-speed scanning of defect detection, this paper proposes a motion-induced eddy current (MIEC) thermography method, which utilizes the relative movement between permanent magnet array and the inspected object to induce the eddy current. Specifically, a set of circumferentially arranged permanent magnets rotating in a steel pipe is used to implement the proposed method. Numerical simulation and experimental studies are conducted to investigate the MIEC and thermal distribution influenced by speed, crack orientation, and lift-off effects. The results show that thermal contrast of the crack rises with its orientation to MIEC flowing path changing from parallel to perpendicular; thermal contrast decreases sharply with the lift-off distance of the magnet array increasing. One merit of the proposed MIEC thermography is that MIEC intensity (thermal contrast) increases with scanning speed and benefits the defect detection.

System identification-based frequency domain feature extraction for defect detection and characterization

Abstract: Feature extraction is the key step for defect detection in Non-Destructive Evaluation (NDE) techniques. Conventionally, feature extraction is performed using only the response or output signals from a monitoring device. In the approach proposed in this paper, the NDE device together with the material or structure under investigation are viewed as a dynamic system and the system identification techniques are used to build a parametric dynamic model for the system using the measured system input and output data. The features for defect detection and characterization are then selected and extracted from the frequency response function (FRF) derived from the identified dynamic model of the system. The new approach is validated by experimental studies with two different types of NDE techniques and the results demonstrate the advantage and potential of using control engineering-based approach for feature extraction and quantitative NDE. The proposed approach offers a general framework for selection and extraction of the dynamic property-related features of structures for defect detection and characterization, and provides a useful alternative to the existing methods with a potential of improving NDE performance.

UHF RFID system for wirelessly detection of corrosion based on resonance frequency shift in forward interrogation power

Abstract: A radio frequency identification (RFID) based system is developed in this paper as a novel passive wireless sensor to detect corrosion for structural health monitoring (SHM) of metallic surface through simulation, design, fabrication and experiment. Firstly, a 2D label-type antenna in a new configuration – circular three arm (CTA) element – is designed as an ultrahigh frequency (UHF) RFID sensor tag working on surface of steel samples. To improve gain and power transmission coefficient, a parasitic element has been added into the centre of CTA. The CTA antenna attached with the parasitic element – so called (CTAP) sensor – has a quality factor notably higher than similar metal mountable UHF RFID antennas that has been resulted to revelation of resonance frequency in measured forward interrogation power. A demonstration and validation system are introduced then to detect corrosion based on the shift of revealed resonance frequency in the forward interrogation power to activate the CTAP tag when placed on surface of corroded and non-corroded steel samples. Results show that the presented system could communicate with sensor tag to detect corrosion up to 2 m of read monitoring range at the UHF RFID standard bandwidth with a sensitivity around of 13 MHz.

Periodic Pulsed Thermography for Inner Defects Detection of Lead-Steel Bonded Structure

Abstract: Multilayer materials with metal–metal bonded structure have been widely applied in aviation, aerospace, and nuclear industry. Disbond is prone to exist in lead-steel bonded structure, which degrades the load capacity and mechanical behaviors. Thermography nondestructive testing is a potential candidate for sub-layer defect detection. However, lead-steel bonded structure is unbearable when undertaken with over-heating of instantaneous temperature, which will lead to subsequent damage or generation of more unpredictable disbond. In addition, detection sensitivity of the deeper defects requires to be enhanced. In this paper, the mathematical derivation and the implementation of the periodic pulsed thermography have been established for detecting inner defects of lead-steel structure. This has been especially conducted for detecting small and deep defects that require high energy to increase detectability. Validation of the proposed method has been undertaken on both inductive thermography and optical thermography. The obtained results have demonstrated that periodic pulsed thermography is highly efficient for deep inner defect inspection of metal–metal bonded structure.

Passive UHF RFID Tag as a Sensor for Crack Depths

Abstract: The use of ultrahigh-frequency (UHF) radio frequency identification (RFID) passive tags for defect detection is a promising application in structural health monitoring. However, it is a challenging task while most related information to tag antenna design is not available as well it suffers from the interference effect on wireless measurements. In this paper, we investigated and developed a new technique for crack depth sensing by using a passive UHF RFID tag as a sensor which interrogated by the ThingMagic M6e platform. The wireless power transfer level and the frequency sweeping are used to match between tag impedance and metal induction effect. The distance between the tag and reader is adjusted at 30 cm which can achieve high quality factor. As a result, the tag backscatter signal becomes rich with maximum peak components. The proposed technique called power peaks feature extraction (PPFE) is used to detect the artificial crack depth on the surface of the stainless steel and ferromagnetic samples. Skewness is applied on PPFE to offer a direct approximation procedure for the crack depth. A linear relationship of skewness achieves high-accuracy result with a maximum estimation error of 0.1 mm for stainless steel sample, the technique is validated and compared with the frequency-domain result, and it achieves all most the same accuracy for the stainless steel sample.

Investigation of Remote Field Eddy Current Defect Detection for Pipeline Welds

Abstract: Due to the roughness and the geometry of the weld region on the pipeline, the defects detection remains as a challenge task by using Nondestructive Testing (NDT) techniques. Remote field eddy current testing (RFECT) has been applied for the detection of the deeper hidden cracks due to its significant penetrate ability. This paper investigates the feasibility study for deeper weld defects detection by using RFECT. In particular, optimizing parameters such as phase rotation and noise reduction have been conducted to enhance the detectability. The impact factor of lift-off is studied to balance the uniformity and intensity of the eddy current distribution. The results have been validated for girth weld defects detection with an extreme deep depth at 9.0 mm of the X70 Pipeline with large lift-off distance at 4.7mm.

An Experimental Comparison of LED and Eddy Current Pulse-Compression Thermography on an Impact Damage CFRP Benchmark Sample

Abstract: A comparison of the application of Pulse-Compression to eddy-current and LED thermography is presented. Results achieved on a dedicated sample with an impact damage are reported and discussed. The same pulse compression procedure based on the use of Barker codes has been applied to both techniques and it proved to be work properly in both cases. Two different features have been extracted and imaged from the retrieved time-responses after pulse-compression.

Monitoring water transfers in limestone building materials with water retention curve and Ground Penetrating Radar: A comparative study

Abstract: The rehabilitation of older buildings is necessary to achieve both a reduction in energy consumption and the preservation of cultural heritage. To ensure a successful building rehabilitation project, an efficient diagnosis makes it possible to determine the various existing pathologies and their causes. In this study, we focus on the “Tuffeau”, which is a kind of limestone widely found in older buildings of the Loire Valley region in France. The durability is strongly affected by the water content for such kind of material. However, very few studies can be found in this field. Moisture condition measurements are currently carried out using punctual sensors placed into the walls. These sensors record highly localized measurements through structural alteration (coring). This paper proposes two non-destructive testing (NDT) methods with the application of Ground Penetrating Radar (GPR) in order to compare the ability of the two methods to analyze the water transfer in limestone blocks. A modified water retention curve model is proposed here to characterize the water gradients in limestone blocks: the limestone - water characteristic (LWC) model. The analysis of the results shows good agreement between the two GPR methods, which shows good capability of monitoring water ingress linking to the results of LWC model.