The aim of this work is to enhance the microwave waveguide-based non-destructive detection of glass fibrereinforced polymer (GFRP) composites by introducing new strategies for probe design and signal processing. The tapering geometry in the waveguide probe design improves the spatial resolution and sensitivity, and the additive manufacturing technique employed reduces the overall cost. In addition, a new approach is proposed for the optimal selection of the inspection frequency in the analysis of the raw frequency-domain data, which achieves high signal contrast. The spatial Fourier transform is introduced to eliminate the undesirable stand-off distance effect in the conventional waveguide-based inspection. Test samples with subsurface grooves and impact-induced damage were examined. It was found that a 1 mm wide groove at a depth of 9 mm and a 10 J barely visible impact damage were well detected and characterised. The results demonstrate the significant potential of microwave testing for the evaluation of composite structures.

Microwave non-destructive testing has been applied for damage detection of glass fibre-reinforced polymer (GFRP) composites, yet practical use remains constrained by insufficient near-field focusing at lower frequencies, lack of effective criteria for optimally selecting an inspection frequency, and the reliance on bulky and expensive vector network analysers. This study introduces integrated X-band (8-12 GHz) inspection strategies combining a dielectric-loaded waveguide, a signal-to-noise ratio (SNR)-based frequency selection method, and a compact standalone measurement system. A dielectric block with a dielectric constant of 10.5 is inserted at the waveguide aperture, improving the spatial resolution while maintaining good penetration. Compared with the principal component analysis (PCA) and K-means clustering, this strategy demonstrates better reliability, particularly in identifying deep defects. Experiments on specimens with variable-depth groove defects and a flat-bottom hole demonstrate enhanced contrast, sharper boundaries, and greater imaging stability compared with an unloaded waveguide probe. The developed system greatly reduces the hardware cost, highlighting a practical, costeffective, and engineering-oriented approach for the maintenance of composite structures.

This study proposes an intelligent microwave open re-entrant cavity resonator sensing system integrated with a 3D-printed structure for high-precision, low-cost, non-destructive thickness measurement of thin coatings on carbon fiber-reinforced polymer (CFRP) composites. Electromagnetic simulations and impedance perturbation theory indicate that the resonance frequency shift is proportional to the coating thickness. Measurements performed on multiple 1 mm thick plastic films revealed resonance frequency shifts exceeding 550 MHz in all cases, demonstrating high sensitivity. A multilayer perceptron neural network achieved a mean prediction error of less than 1.70%, suggesting that the sensing is not dependent on the sepcific material. The custom-designed hardware system, which replaced a vector network analyzer, achieved prediction errors within ±10 μm for PET coatings with thicknesses up to 864 μm and enabled precise measurement of real paints.

This study addresses the challenges of strong calibration dependence and operational complexity in measuring the thickness of coatings on carbon fibre-reinforced polymer (CFRP) composite surfaces. The system utilizes an open cylindrical cavity resonator, achieving miniaturization through the incorporation of a high-permittivity dielectric material, which significantly enhances portability. The sensor performs thickness inversion by leveraging the linear relationship between the resonant frequency shift and the coating thickness. Experiments demonstrate that this relationship is independent of the coating material type and exhibits high consistency across three substrate types: CFRP, aluminum plate, and CFRP with a copper mesh, with coefficients of determination (R2) reaching 0.9979, 0.9985, and 0.9976, respectively. This enables accurate thickness measurement of both uncalibrated coating materials and multi-layer coating stacks on each substrate type after performing a single calibration for that specific substrate. The system shows measurement errors of less than 2 % for various simulated coating materials. Integrated thickness prediction software enables automated extraction of the resonant frequency and real-time output of thickness values.

The Structural Health Monitoring (SHM) of critical metal components is indispensable to ensure the safety service of major facilities and the efficient operation of high-end equipment. The flexibility and environmental adaptability of existing sensors and measurement techniques have become key challenges restricting the SHM applications and practices. Radio Frequency Identification (RFID) sensors obtain structural parameters through electromagnetic induction, which have become a promising solution due to the advantages of passive, wireless, easy deployment, flexible and convenient. This paper presents a novel Planar Inverted-F Antenna (PIFA) RFID sensor for metal crack detection. The key contributions include: (1) Modeling of the antenna-coupling sensing mechanism for crack measurement, which clarifies the relationship of structural parameter, antenna impedance, RFID backscatter coefficient, and reader Received Signal Strength Indicator (RSSI) and phase. (2) Development of a rotationally symmetric PIFA structure for crack sensing, meander-line design is employed to reduce its size and Defected Ground Structure (DGS) is introduced to enhance the sensitivity. (3) Proposal of a quantitative crack analysis method based on the RSSI and phase, which utilizes multi-frequency sensitivity and monotonicity analysis for optimal frequency selection and crack angle identification, and a polar coordinate curve fitting is employed for crack width estimation. Finally, experimental studies are conducted, and the results verified the effectiveness of the presented sensor and data processing method for metal crack detection. The proposed method has the advantages of being passive, wireless, miniaturized, easy deployment, and high sensitivity, which provides a valuable reference for future intelligent SHM research and practice.

The structural damages of metallic structure components in many critical facilities and equipment may result in disasters that endanger human life. Existing Structural Health Monitoring (SHM) solutions commonly suffer from the limitations of bulky equipment, poor environmental adaptability, and high costs, which raise challenges for detection efficiency and large-scale multi-target monitoring. Radio Frequency Identification (RFID) sensing technology, featuring Non-Line-of-Sight (NLoS), flexible and pasteable, and easy deployment, show great promise for SHM. Recent studies have demonstrated the potential of RFID sensors for structural damage sensing including cracks, strain, and corrosion of metal structures, along with the analysis of parameters like crack width, structural deformation, and corrosion depth. This study provides a survey and in-depth analysis of recent technical progress in RFID sensor-based SHM. The main contributions include: (1) Classification of the novel sensing techniques and systems based on the functional model of RFID backscatter sensing; (2) Summarization of the common structural damage types and the feature extraction techniques of RFID sensing for SHM; (3) Survey of the recent progresses of the techniques, methods, and applications for RFID-based SHM; (4) Analysis of the challenges facing the state-of-the-art, including characterization and quantification of structure damage parameters and the impact of environmental factors, followed with an outlook of the future work. This study provides a timely reference for the innovation and practice of RFID sensing techniques in the field of SHM.

For the advantages of noncontact sensing, inventory identification, and cost-effective deployment, radio frequency identification (RFID) localization has become a promising solution for some industrial Internet of Things (IoT) applications. However, its efficacy is often constrained by multiantenna dependency, motion-induced phase distortions, and the inherent phase coupling between position and orientation, all of which hinder simultaneous detection of orientation and position with high accuracy. To overcome these issues, this investigation proposes a novel phase-decoupling model specifically designed for a single-antenna synthetic aperture radar (SAR) RFID system. The key contributions include: 1) design and implementation of an adaptive dynamic phase compensation (ADPC) mechanism for decoupling motion-induced parameters from target backscatter signatures, effectively mitigating phase offsets caused by non-steady-state antenna trajectories; 2) establishment of a novel phase-orientation model and a differential phase-adaptive peak detection (DPAPD) framework, which integrates differential measurements with threshold-optimized peak identification, achieving subdegree angular resolution; and 3) development of a 3-D SAR localization method incorporating phase decoupling and particle filter (PF) which achieves robust and consistent 3-D localization with acceptable accuracy. This investigation provides a high-accuracy and cost-effective dynamic monitoring solution for RFID-based smart shelves, enabling advanced applications such as inventory tracking and tilt detection for fragile goods in automated warehouses.

Glass fibre-reinforced polymer (GFRP) composites have been widely used in aerospace, wind energy, marine and various other industries for their high stiffness and strength. However, the structural integrity can be significantly compromised by defects introduced during manufacturing and field use. This work presents a novel microwavebased non-destructive detection approach using an open-ended rectangular waveguide loaded with a thin dielectric slab. With this configuration, the electromagnetic field is concentrated compared with an empty waveguide, leading to improved spatial resolution. The theoretical analysis and design strategy for the slab are presented in detail. A new signal processing method is proposed for the optimal selection of inspection frequency. In the test, a 1 mm thick dielectric slab with a dielectric constant of 16 was centred in a Ka-band (26.5-40 GHz) rectangular waveguide. Subsurface grooves (1 and 2 mm wide), flat-bottom holes (2 mm in diameter with depths ranging from 1 mm to 9 mm) and 10 J barely visible impact damage were successfully detected. The effectiveness of two other imaging methods, namely time-domain reflectometry and synthetic aperture focusing, is well demonstrated. The defects can also be readily identified in the B-scan, C-scan and 3D time-domain images produced. The synthetic aperture focusing technique significantly enhances the signal contrast.

Radio-Frequency Identification (RFID) positioning promises a prospective future for industrial automation and Industrial Internet of Things (IIoT) applications. However, the radio waves carry multiple parameters including position, orientation, and ambient environment factors, which raises challenges in simultaneous detection of position and orientation of product objects. This investigation proposes a simple RFID array-based position and orientation simultaneous detection technique for moving object in industrial chain. The main contributions of this investigation include: (1) Theoretical analysis and integrated model of position and orientation variation with the antenna parameters and interrogation variables in RF backscatter coupling-based sensing. (2) Development of an innovative simple RFID array-based phase separation technique with differential sensing, which determines the position-and orientation-induced phase without their mutual coupling impact. (3) Proposal of a simultaneous detection technique for moving objects’ position and orientation by integrating the Multiple Signal Classification (MUSIC) algorithm and hyperbolic positioning algorithm. In the experimental verification with a range from -75 cm to 75cm, the average error of position and orientation estimation is 4.29 cm and 4.89 degrees.

In this article, a comprehensive overview of microwaves-based non-destructive testing (NDT) techniques for carbon fibre- and glass fibre-reinforced polymer composites is presented. These lightweight composites have been widely employed in aerospace, naval, automotive, construction, electronics and wind energy industries. Monitoring the structural integrity is critical for the maintenance and repair of such heterogeneous composite structures. Traditional ultrasonic methods do not always identify defects or damage in such structures. In that case, microwave NDT techniques can provide a complementary modality. The microwave NDT has been adopted for material characterisation, quality assessment and damage detection. Wider applications will be expected, as more low-cost microwave components become commercially available.

A novel non-destructive testing scheme was proposed for the detection of impact damage in glass fibre-reinforced polymer (GFRP) composites using a microwave planar resonator sensor. The sensor offers the advantages of small size, low cost and simple structure. It is an open-circuited lambda/2 long microstrip line and the detection principle is material perturbation. Electromagnetic simulation verifies the sensor design. A GFRP specimen subjected to 5,10 and 20 J impact was examined. The 20 J impact damage was detected through line and two-dimensional scanning. The line scanning enabled accurate localisation of the damage, whereas the two-dimensional scanning facilitated more precise reconstruction of the surface damage features in addition to localisation. The sensor performance for detecting impact damage with lower energy levels was investigated by line scanning. It was found that the sensor could detect and locate 10 J impact damage. Principal component analysis was introduced to significantly reduce the false detection of the 5 J impact damage. It is well demonstrated that the proposed scheme could serve as an alternative method.

A novel microwave coaxial line cavity resonator sensor has been developed to measure the thickness of coatings on carbon fiber-reinforced polymer (CFRP) and glass fiber-reinforced polymer (GFRP) composites, which are widely used in aerospace and wind energy industries. Due to the symmetric electromagnetic field distribution, the sensor is insensitive to the anisotropy of the composite. Unlike the existing microwave sensors that only work for CFRP, this sensor demonstrates promising results for GFRP composites, expanding its applicability. Experimental results show a consistent increase in the resonance frequency with increasing coating thickness, with prediction errors of 1.87% for CFRP and -0.40% for GFRP, respectively. Furthermore, a wireless measurement system, including a low-cost, portable microwave analyzer and a single-board computer, is proposed to enhance the use for on-site coating assessment of fiber-reinforced polymer composites.

Enhanced by the simultaneous wireless power and data transfer, the Radio Frequency Identification (RFID) techniques create new chances for the Internet of Things (IoT), which is used to integrate sensitive elements for different sensing purposes. However, the sensor-augmented RFID tags and chip-less RFIDs suffer from the limited power supply and the impact of ambient environments, respectively. This investigation explores the commercial RFID-based sensing techniques to obtain the physical parameters of the RFID-labelled objects with differential sensing via the backscatter coupling between the reader-tag antennas. The main contributions of this investigation include: (1) analysis of RFID backscatter coupling and the establishment of the relational model of measured parameters, antenna parameters, and RFID Received Signal Strength Indicator(RSSI)/phase; (2) simulation studies of the electromagnetic coupling between the RFID tag and Material Under Test (MUT) to reveal the relationship of the MUT parameters with antenna impedance; (3) design of an RFID tags differential sensing scheme to compensate for the environment impact using a shielding layer; (4) determination of sensitive frequencies with Principal Component Analysis (PCA) and measurand reconstruction method with Levenberg-Marquardt (LM) curve fitting. Finally, the experimental studies are conducted with NaCl solution sensing. The results show that the presented techniques can effectively obtain the change in the solution’s concentration, which demonstrates the effectiveness of the proposed techniques.

非金属(如玻璃钢、聚乙烯、聚氯乙烯、聚丙烯等)管道因具有成本低、耐腐蚀性能强等优点,近年来在石油、化工、排水、核电、燃气输送等领域逐渐代替了金属管道.但是其在制造和使用过程中会不可避免地出现缺陷和损伤,因此及时有效地检测材料结构显得至关重要.与其他常规管道检测技术相比,微波技术具有无损、不需要耦合剂、低功耗、非接触、易于操作、无电离辐射等优势,可用于评估结构缺陷、损伤、材料电磁性能等,在评估管道结构完整性方面效果良好.介绍了微波检测技术的发展历程,总结了典型检测方法及应用,特别是近10年来在非金属管道缺陷检测方面的研究进展,最后,分析了现有微波检测非金属管道存在的问题,探讨了未来非金属管道检测技术的发展趋势.

Tooth diseases including dental caries, periodontitis and cracks have been public health problems globally. How to detect them at the early stage and perform thorough diagnosis are critical for the treatment. The diseases can be viewed as defects from the perspective of non-destructive testing. Such a defect can affect the material properties (e.g., optical, chemical, mechanical, acoustic, density and dielectric properties). A non-destructive testing method is commonly developed to sense the change of one particular property. Microwave testing is one that is focused on the dielectric properties. In recent years, this technique has received increased attention in dentistry. Here, the dielectric properties of human teeth are presented first, and the measurement methods are addressed. Then, the research progress on the detection of teeth over the last decade is reviewed, identifying achievements and challenges. Finally, the research trends are outlined, including electromagnetic simulation, radio frequency identification and heating-based techniques.

A new method using a microwave coaxial line resonator sensor for the non-destructive evaluation of defects in polyethylene pipes is presented. The sensor consists of a coaxial line section and an extended section of circular waveguide. One end is short-circuited and the other end is open to free space. The sensor aperture is designed to conform to the curvature of the pipe outer surface, so that the common problem of the stand-off distance effect by open-ended waveguide scanning is well circumvented. The presence of defects causes local permittivity and volume changes, leading to variations of the resonance frequency and quality factor due to material perturbation. The detection principle is confirmed by electromagnetic simulation. From the experiments, it is shown that the proposed sensor can perform both detection and classification of flat-bottom holes. The minimum diameter of the hole that can be detected is 1 mm, which is lower than that required in the standard practice. This method can also effectively detect axial holes and determine the lengths. The sensor system presented here has the advantages of low cost, convenience for on-site detection and quantitative analysis.

A microwave microstrip line resonator sensor is developed as an alternative tool for detecting adulteration in honey. A honey-filled tube is placed at the position with the maximum electric field intensity. When the honey is adulterated, its permittivity is changed, leading to a distinct resonance frequency shift and enabling detection. Compared with the existing microwave sensors, this sensor offers the advantages of low cost, compact size, and easy fabrication. Moreover, quantitative analysis of the adulteration could be achieved. Electromagnetic simulation is performed using a co-simulation with CST and MATLAB. The simulation results reveal that the resonance frequency of the resonator decreases as the added water content increases, following a quadratic polynomial relationship. In the experiments, the results demonstrate a successive decrease in the resonance frequency from the empty tube, honey-filled tube to water-filled tube cases. Furthermore, honey samples with varying water contents (up to 70%) are tested, and the resonance frequency decreases with increasing added water content, which agrees well with the simulation results. In addition, there is a quadratic relationship between the two parameters. Principal component analysis is conducted on the transmission coefficients, and the first principal component decreases with increasing water content. With the addition of the second principal component, the cases of different water contents in honey can be well classified.

Accurate thickness measurement of thin coatings (typically 50–500 μm) on carbon fibre-reinforced polymer composites is a major challenge in the manufacturing and maintenance processes of modern aircraft. Different from the conventional material-dependent technique for prediction, a machine learning-enabled strategy with an artificial neural network configuration is used with no requirement of prior knowledge of the type of coating or substrate under test. In the test, an open microwave cavity resonator sensor is directly placed on a coated composite, and any variation of the coating material, coating thickness and conductivity of the composite alters the resonance frequency. Principal component analysis is employed in the signal pre-processing for the dimensionality reduction of the raw measurement data. In terms of the root-mean-square error, the maximum value for the calibration approach is approximately 15 μm and that for the machine learning-based approach is 12 μm. The sensor system developed enables real-time on-site assessment of coated composite structures and thus offers a new approach for non-destructive evaluation 4.0 with improved efficiency, accuracy and automation.

Evaluation of lubricating oil is critical to the operation and maintenance of industrial engines. Here a microwave rectangular cavity resonator sensor is developed for condition monitoring. An oil-filled quartz tube is placed at the position of maximum electric field intensity, where any impurity in the oil perturbs the resonance frequency and quality factor. This arrangement enables the detection capability of nonmetallic contaminants. Particle types are readily differentiated from the resonance parameters due to the different permittivity characteristics of the mixtures. The effect of the contamination on the permittivity is also revealed, providing an alternative analysis approach. From the measurement of standard liquids, the optimal permittivity calculation algorithm for the system developed is found, and estimation errors within ±5% are achieved. The use for oil adulteration detection is also demonstrated. The multifunctional sensor is low-cost, scalable, easy to implement, and capable of on-site and online monitoring.