Non-destructive Monitoring Technique Research Articles

Ductility and cracking behavior of UHPC-RC composite beam under bending test based on acoustic emission parameters

Ultrahigh-performance concrete (UHPC) possesses improved mechanical properties due to its high strength, durability, and toughness. Currently, there is a single method for detecting the cracking behavior and evaluating the ductility of the UHPC-reinforced concrete (RC) composite beam. As an important part of structural non-destructive monitoring techniques, the acoustic emission (AE) technique can be used to analyze the damage pattern of the structure by the variation characteristics of its individual parameters. On this basis, four-point bending tests were carried out on the RC beam and UHPC-RC composite beam, and the AE technique was used for ductility analysis and cracking behavior monitoring. The results showed that the UHPC material enhanced the flexural capacity of the UHPC-RC composite beam. The AE ring counts and energy parameters could be used to analyze the cracking process of the UHPC-RC composite beam during flexural damage. The proposed new ductility indices, AE ring counts ductility index (ARD) and AE energy ductility index (AED), differed from the displacement ductility indices within the range of 2 %, providing a new method for calculating ductility indices of concrete structures. During the failure stage, the UHPC-RC composite beam produced more tensile cracks at the early stage of cracking and the stage of damage compared to the RC beam. In addition, the b value of the RC beam had a large range of fluctuations during the failure stage, indicating that the RC beam produced more micro-cracks and macro-cracks and more damage compared to the UHPC-RC composite beam.

Deformation stage identification in steel material using acoustic emission with a hybrid denoising method and artificial neural network

Acoustic emission (AE) is widely used for identifying source mechanisms and the deformation stage of steel material. The effectiveness of this non-destructive monitoring technique heavily depends on the quality of the measured AE signals. However, the AE signals from deformation are easily contaminated by the signals from noise in a noisy environment. This paper presents a hybrid model for deformation stage identification, which combines a self-adaptive denoising technique and an Artificial neural network (ANN). In pursuit of model generality, AE signals were collected from tensile coupon tests with various steel materials and loading speeds. First, a decomposition-based denoising method is applied based on the singular spectral analysis (SSA) and variational mode decomposition (VMD), which is defined as SSA-VMD. Its effectiveness is demonstrated by simulated signals and experimental results. Following the use of the denoising technique, an ANN is constructed to identify the deformation stage of steel materials with the input of features extracted from the filtered AE signals. The results indicate that the ANN achieves a high prediction accuracy of 0.93 in the test set and 0.87 in unseen data. By applying this denoising method, the ANN-based approach enables accurate correlation of the collected AE signals to deformation stages. The finding can be used as the basis for the creation of new methodologies for monitoring structural health status of in-service steel structures.

Assessment of the effect of embedded RFID tag on a composite laminate strength

RFID (Radio Frequency Identification) is commonly used to monitor goods along the supply chain. The feasibility of the application of a RFID tag to monitor CFRP components was demonstrated in a previous work by some of the authors. This work is aimed at evaluating how the integration of a RFID tag inside a CFRP laminate affects the quasi-static strength and the failure mode. Finite element modelling is used to design a specimen with an embedded tag, that may be representative of the tensile state of stress in a larger laminate. Tensile testing is performed both up to failure and by interrupting tests at different load levels in order to inspect the specimen by C-scan. Acoustic Emission (AE) and Digital Image Correlation (DIC) are used as additional non-destructive monitoring techniques. Some interrupted tests were selected to extract micrographic sections that illustrate damage development in the laminate. The extensive destructive and non-destructive characterization allowed to quantify the effect of embedding a RFID tag in the laminate in terms of strength decrease and failure mode.

Investigation on fatigue behavior of GFRP hybrid bonded/bolted joint under shear loading based on nondestructive monitoring test and numerical modeling

The GFRP (Glass Fiber Reinforced Polymer) is prospective in bridge and building engineering for its advantageous material property. The connection configuration of hybrid bonded/bolted joint of GFRP is thought superior in mechanical performance compared with the traditional bonded and bolted joints. However, the fatigue issue of this type of joint remains unclear which impedes its engineering applications. The presented work experimentally and numerically investigated the fatigue behavior of the GFRP hybrid bonded/bolted single-lap joints under shear loading. High-cycle fatigue tests on single-bolt, four-bolt, and nine-bolt specimens were conducted to study the failure process, rigidity degradation, failure mode, and fatigue life of the hybrid joints, where the nondestructive monitoring techniques of AE (Acoustic Emission) and 3D-DIC (Three-Dimensional Digital Image Correlation) were adopted, respectively to detect the material damages and overall deformation of specimens. Finite element modelling was further carried out to reveal the fatigue failure mechanism of hybrid joints, in which the property degradation of GFRP plates and adhesive layer were considered. Results showed that the failure process of GFRP hybrid single-lap joints under fatigue shear loading can be divided into four stages: (1) steady and (2) rapid development of adhesive damage, (3) steady and (4) rapid development of GFRP damage, of which the (2) and (4) stages account for less than 3% of the total fatigue life of joint, respectively. Stiffness degradation of 14–35% were read for the tested joints before final failure, and the joints with more bolts performed a superior degradation resistance compared to the less ones. Besides debonding and bolt inclination, the single-bolt joints failed with bearing failure of GFRP material, while the failure mode of multi-bolt joints were dominated by shear failure in GFRP plate with Y-shaped zone. The simulation results well supported the observations in experimental test. Based on the experimental results, the S-N curve of the GFRP hybrid single-lap joints is proposed, which can provide support for the engineering design of this type of joints.

Non-destructive monitoring and prediction of fouling by organic matters and residual anionic coagulant during membrane process

Physical fouling characteristics on silicon carbide (SiC) membranes induced by various organic matter compounds vary depending on the presence of calcium ions (Ca2+). Both destructive techniques (morphological surface analysis) and non-destructive techniques (fouling properties monitoring) were used to determine the fouling mechanisms and behavior during the membrane filtration systems. Destructive analysis and a modified Hermia model were employed to assess the fouling mechanisms. Fouling behavior was also analyzed through non-destructive monitoring techniques including optical coherence tomography (OCT) and three-dimensional laser scanning confocal microscopy (3D-LSM). At concentrations of 10, 30, and 100 mg/L without Ca2+, the flux decreased by 57–95% for humic acid (HA) and anionic polyacrylamide (APAM). APAM exhibited a notable removal rate of up to 56% without Ca2+. At concentration of 10, 30, and 100 mg/L in the absence of Ca2+, the flux decreased by 6–8% for sodium alginate (SA). However, the addition of Ca2+ led to a reduction in the flux for SA by up to 91% and resulted in a removal rate of 40%. Furthermore, addition of Ca2+ led to an alteration of the fouling characteristics of HA and SA. In the case of HA, higher concentrations resulted in elevated thickness and roughness with correlation coefficients of 0.991 and 0.992, respectively. For SA, increased SA concentration led to a thicker (correlation coefficient of 0.999) but smoother surfaces (correlation coefficients of 0.502). Monitoring of these physical characteristics of the fouling layer through non-destructive analysis is crucial for effective fouling management, optimization of the system performance and extending the lifespan of the membrane. By continuously assessing the fouling layer thickness and surface roughness, we expect to be able to provide insights on the fouling behavior, identify trends, that can help scientists and engineers to make informed decisions regarding fouling control strategies in future.

Monitoring corrosion-induced concrete cracking adjacent to the steel-concrete interface

Substantial research effort has been devoted on linking corrosion-induced cracking of concrete with the internal corrosion damage level. Still, numerical models of the corrosion and cracking process require internal parameters, that cannot be directly evaluated from experimental data. Therefore, this study provides a novel experimental method for monitoring the effects of steel corrosion adjacent to the steel-concrete interface. This non-destructive method is suited for small-scale laboratory-made specimen, and was designed to provide missing information required for subsequent calibration of numerical models. Hollow steel bars were cast into concrete and subjected to accelerated corrosion using the impressed current technique. The deformations of the hollow steel bars were measured using distributed strain sensing in an optical fibre, attached to the inner surface of the hollow steel bars. After the corrosion period, X-ray Computed Tomography scans were performed to evaluate concrete cracking and corrosion level. The results reveal a non-uniform distribution of strain around the perimeter of the steel, indicating a non-uniform radial stress distribution. The non-uniformity correlated very well with the position of the corrosion-induced cracks; with extension hoop strains in the steel at the location of these cracks and contraction hoop strains in between. Further, the corrosion level varied around the perimeter, with higher values near cracks. The combination of non-destructive monitoring techniques used in this study on small-scale laboratory-made specimens show great potential to reveal new insights on how the corrosion pattern, corrosion-induced cracking of the concrete cover and stress (indirectly measured through the strain in the steel) interact throughout the corrosion process.

Influence of Biaxial Stress on Magnetic Behavior of Hot-Rolled Steels

On-line non-destructive magnetic monitoring (NDM) techniques are relevant to evaluate the quality of steels and their fluctuations during forming process. The on-line monitoring faces different issues. Due to magneto-mechanical coupling, stress that may be applied during NDM may have a strong effect on the magnetic behavior that may lead to erroneous estimations of the microstructure quality of the material. The multiaxial character of the stress state is another strong issue. In this communication, some new results showing the effect of a uniaxial and biaxial stress on the magnetic behavior of 3 grades of hot-rolled steels are presented (from low carbon steel to AHSS).

An Experimental Study on Acoustic Emission of Broken Wires in Steel Strand

Acoustic emission technology, as a nondestructive real-time monitoring technique, has been proven to be an effective method for monitoring dynamic damage of materials. The prestressing strand material used for constructing bridge ties is the focus of this study. This paper reports the experiment setup in detail. This test uses an oil pump to load the prestressing strand at a uniform rate for the tensile breakage test. Real-time signal acquisition of the strand damage during the tensile test was performed using acoustic emission equipment. The preliminary analysis of the data was used to determine the effect of amplifier and transducer type on signal acquisition.

Weld depth dynamics measured with optical coherence tomography during remote laser beam oscillation welding of battery system

This paper represents nondestructive quality monitoring technique using optical coherence tomography (OCT). It addresses online monitoring of weld depth during laser beam oscillation welding and aims at the application in joining cells in large battery assemblies. The weld depth was continuously detected with OCT while the OCT beam position was adjusted highly dynamically in accordance with the scanning optics position. By displacing the OCT measurement beam according to the current machining direction, the correlation between the position of the laser beam in an oscillating circular pattern along the circular feed direction and the periodic fluctuations of the measured weld depth was explored. It was found that the deepest part of the keyhole is located at the trailing position of the laser beam. This effect can be attributed to the large heat input due to the overlapping circular movements. The results confirm once again that instant weld depth monitoring with OCT ensures superior weld quality.

Cross‐disciplinary insights into the mechanisms of plant cold hardiness: From molecules to ecosystems

Cross‐disciplinary insights into the mechanisms of plant cold hardiness: From molecules to ecosystems

Forecasting compressive strength and electrical resistivity of graphite based nano-composites using novel artificial intelligence techniques

To produce highly effective electrically conductive cementitious composites and advance non-destructive structural-health monitoring techniques, graphite-based nanomaterials (GrNs) are viable conductive fillers. A comprehensive database was created for CS and ER pertinent to GrNCC’s experimental studies from open sources. It is a challenging multi objective optimization problem (MOOP) due to the requirement of both mechanical strength and electrical resistivity for the design of Graphene nanoparticles-reinforced cementitious composites (GrNCC). The current study provides an ample data-driven approach employing machine learning (ML) techniques and a non-dominated sorting genetic algorithm (NSGA-II) to address this multi objective design optimization (MODO) problem for GrNCC. ML theories quantify how important parameters affect GrNCC’s qualities. First, using processed experimental datasets, gene expression programming (GEP) is used to develop prediction models for GrNCC’s uniaxial compressive strength (CS) and electrical resistivity (ER). The findings demonstrate its superior predictive accuracy, as evidenced by high R2 of 0.92 and 0.91 and low mean absolute error (MAE) scores of 1.57 MPa, and 60.46 kΩ.cm for CS and ER, respectively. For the MODO technique, parameter analysis was performed, which aids in identifying the parameters that need to be optimized and defining their constraints. Finally, NSGA-II is used as the foundation for the MODO technique. With the proposed prediction models as objective functions, it simultaneously optimizes the CS and ER of the GrNCC. It successfully produces a collection of Pareto solutions that make it easier to choose the right parameters for the GrNCC design. The proposed MODO technique can be efficiently applied to multiobjective design and optimization of electrical and mechanical properties of GrNCC.

In-service wind turbine blade monitoring

Monitoring wind turbine blades for damage onset and progression detection in efficient and effective ways can be one of the most challenging tasks for wind site managers, technicians, and fleet engineers. Blades are susceptible to damage from a variety of causes and sources, ranging from environmental factors such as lightning to normal wear-andtear leading to worsening leading edge erosion, crack propagation and internal delaminations. With these defects often out of sight, particularly with inspections only being conducted once a year or less, staying fully aware of true blade integrity is often not as efficient a process as it could be. Acoustic Emission (AE) technology is a passive non-destructive monitoring technique that has been successfully used developed to help solve some of the most challenging tasks in the wind energy sector and helping customers stay aware of the integrity of wind turbine blades while maximizing turbine uptime. The MISTRAS solution offers a unique solution for blade integrity management through 24/7/365 remote monitoring and data reporting. MISTRAS combines industry-leading Acoustic Emission (AE) technology with an easily accessible data portal to help protect your blades at every phase of their operations. This work will present a background on the work that has been conducted to enable the technique to be successfully applied. Case studies of real, in-service applications will be presented along with examples of the types of defects detected. This work will present a novel AE monitoring system that has been constructed by MISTRAS for the sole purpose of wind turbine monitoring. An overview of the data acquisition system and sensors are presented followed by the data communications and analytics. Case studies on that demonstrate damages that have been identified of the system in operation are also presented, demonstrating the ability of the system to detect damage early and direct maintenance operations.

Online monitoring of oil film thickness of journal bearing in aviation fuel gear pump

As the key support of aviation fuel pumps, journal bearings are operated under extreme conditions of high temperature, high pressure and low viscosity media, therefore, the broken of the ultra-thin lubrication film is the main cause of lubrication failure and abnormal wear. However, the dynamic hydraulic lubrication (DHL) film is still blind due to the lack of non-destructive online monitoring techniques. This paper presents the study of ultrasonic-based measurement for the dynamic oil film thickness of fuel pump. The circumferential oil film thicknesses in a journal bearing are measured with the complex model and the resonance model, meanwhile, the minimum oil film thickness is extracted by fitting the measured points with the initial circle. Under varying operating conditions, the measured results are verified with classical DHL simulations, and the maximum relative error is shown to be lower than 15%.

Identification of mixed mode damage types on rock-concrete interface under cyclic loading

The study of the mixed mode Ⅰ-Ⅱ fracture damage characteristics of the rock-concrete interface under cyclic loading is essential to grasp the operation of gravity dams under seismic loading. To reveal the mixed mode fracture damage characteristics of the rock-concrete interface under cyclic loading, a four-point shear (FPS) test for rock-concrete specimens was carried out at first. The entire fracture damage process of rock-concrete specimens was monitored in real-time by acoustic emission (AE) and digital image correlation (DIC), the widely used nondestructive monitoring techniques. The mechanical test results show that the modal angle is related to the stiffness decay of rock-concrete specimens. The quantitative identification method of tensile and shear damage based on machine learning algorithm can quantitatively identify the damage types at the interface of rock-concrete specimens. The influence of rock type and modal angle on the interface damage mechanism was quantified by the damage identification method, and the spatial and temporal evolution of tensile and shear damage with cyclic loading was determined. The spatial and temporal evolution characteristics of the damage were compared with the DIC strain contours of the corresponding stages to reveal the mechanism of the influence of the damage evolution law on the crack expansion path.

Recent Advances in Nondestructive Method and Assessment of Corrosion Undercoating in Carbon–Steel Pipelines

Carbon–steel pipelines have mostly been utilized in the oil and gas (OG) industry owing to their strength and cost-effectiveness. However, the detection of corrosion under coating poses challenges for nondestructive (ND) pipeline monitoring techniques. One of the challenges is inaccessibility because of the pipeline structure, which leads to undetected corrosion, which possibly leads to catastrophic failure. The drawbacks of the existing ND methods for corrosion monitoring increase the need for novel frameworks in feature extraction, detection, and characterization of corrosion. This study begins with the explanations of the various types of corrosion in the carbon–steel pipeline in the OG industry and its prevention methods. A review of critical sensors integrated with various current ND corrosion monitoring systems is then presented. The importance of acoustic emission (AE) techniques over other ND methods is explained. AE data preprocessing methods are discussed. Several AE-based corrosion detection, prediction, and reliability assessment models for online pipeline condition monitoring are then highlighted. Finally, a discussion with future perspectives on corrosion monitoring followed by the significance and advantages of the emerging AE-based ND monitoring techniques is presented. The trends and identified issues are summarized with several recommendations for improvement in the OG industry.

Development of a Nondestructive Monitoring Technique for Vial Freeze-Drying Process using Microwave Resonance Spectroscopy

In this study, we developed a nondestructive monitoring technique for the drying progress of products in vials during the freeze-drying process using microwave resonance spectroscopy. An aqueous mannitol solution, commonly used as an excipient for injectable drugs, was used as the model material. The drying progress was determined using a mathematical model (i.e., Rp−Kv model), in which the parameters of mass and heat transfer were experimentally determined while considering the variation of the drying progress and position of the product on the drying shelf. A strong correlation was found between the extent of drying and changes in the microwave resonance spectrum. The simulation results were regressed against the changes in the resonance peak frequency and resonance intensity. Using this regression equation, the extent of drying could be predicted from the acquired spectral data. In this way, we were able to estimate the design space (the relationship between the operating conditions, internal temperature of the product, and sublimation rate), which changed with the extent of drying from the spectrum obtained inline, showing that it is possible to set the appropriate operating conditions based on the spectra.

Implementation of Non-Destructive Condition Monitoring Techniques on Low-Voltage Nuclear Cables: II. Thermal Aging of EPR/CSPE Cables

Determining the aging state of low-voltage nuclear power plant cables using a nondestructive and reliable condition monitoring technique is highly desirable as the cables experience multiple aging stresses during the service period. This paper deals with the implementation and investigation of such nondestructive techniques, which can detect the overall aging state of low-voltage instrumentation and control (I&C) cables, which are subjected to accelerated thermal aging. The dielectric spectroscopy, extended voltage response, and polarization–depolarization current as nondestructive electrical aging techniques were used for the investigation purpose, while the elongation at break was also adopted as a mechanical measurement and for comparison. Prominent variations in the electrical parameters for the insulation and jacket were observed, whereas the elongation at break for both materials also decreased under thermal aging. Based on the electrical techniques, aging markers were selected that showed a strong correlation with the aging and elongation at break, proving the ability of the adopted electrical methods as a nondestructive condition monitoring technique.

Wind turbine ice detection using hyperspectral imaging

Wind energy has been playing a pivot role in replacing the traditional energy sources. This emerging paradigm has proved itself as a good candidate among all renewable energy sources. Although the exponential growth of the wind industry, wind turbines still suffer from blade icing especially in cold regions. Blade icing disturbs aerodynamic performance and results in power losses, safety risks, mechanical and electrical breakdowns, measurement, and control faults. Anti-icing and de-icing techniques mitigate these adverse effects. It is mandatory to rigorously evaluate the meteorological operating conditions during the assessment phase to determine the need and advantages of installing an anti-icing or a de-icing system. Moreover, this diagnostic is also essential during the operation to detect icing, prevent failure, and enhance production. Different ice detection methods, such as double anemometry, vane, relative humidity, and dew point, are used. These techniques have few drawbacks that can be overcome using hyperspectral imaging. This paper offers an overview of icing detection technologies and explores spectroscopy imaging applications for detecting ice accretion in wind farms. This study describes the application of this non-destructive and fast monitoring technique in remote sensing of icing incident on a wind turbine blade. This paper outlines the experimental approach conducted on a blade sample with an ice-covered portion. The icing model, on which this detection method is based, is designed, simulated, and confirmed to acquire enhanced blade icing knowledge. The hyperspectral imaging validation results for icing occurrence detection in their initial development phases are satisfactory. The experimental findings of this technique reveal that the accuracy and precision of blade icing detection are considerably enhanced.

Multi scale aging assessment of low-voltage cables subjected to radio-chemical aging: Towards an electrical diagnostic technique

In this article, the aging behavior of nuclear-grade low voltage cables, characterized by different geometries and insulation compositions, is investigated. Cables were subjected to radio-chemical aging at different dose rates (7 Gy/h, 66 Gy/h and 400 Gy/h), in order to simulate typical aging environments inside nuclear plants. The changes of insulation properties due to aging are investigated at different scales, aiming at highlighting possible correlations between molecular-scale properties and global macroscopic material behavior (e.g., mechanical and electrical ones). Microscale material behavior is investigated by means of FTIR spectroscopy and oxidation induction time (OIT) measurements, in order to evaluate material composition changes and material resistance to oxidation, respectively. On the other side, mechanical and electrical macroscopical properties are examined through tensile stress and dielectric spectroscopy measurements. It is found that aging is deeply influenced by the effect of additives (e.g. antioxidants) inside the insulation. In particular, the presence of antioxidants delays oxidation process allowing material modifications during the early aging states to be evaluated. Dielectric spectroscopy is demonstrated to properly follow all the stages of the degradation process, confirming its appropriateness as a non-destructive condition monitoring technique for cables. Finally, the evolution with aging of the dielectric response is associated with the variations of the considered chemical and mechanical properties, allowing the derivation of correlation master curves.

Aging Mechanisms and Non-Destructive Aging Indicators of XLPE/CSPE Unshielded LV Nuclear Power Cables Subjected to Simultaneous Radiation-Mechanical Aging.

Low-voltage cable systems in nuclear power plants are key components that have a crucial role in the safe operation of nuclear facilities. Thus, the aging management of cable systems is of utmost importance as they cannot easily or economically be replaced or upgraded. Therefore, there is a continuous need to develop reliable non-destructive condition monitoring techniques, mostly based on the measurement of the dielectric properties of cable insulation. This paper introduces the changing of dielectric and mechanical properties of XLPE insulated and CSPE jacketed unshielded low-voltage nuclear power plant power cable in case of simultaneous mechanical and radiation aging. The cable samples were bent and exposed to 400 kGy gamma irradiation with a 0.5 kGy/hr dose rate. Dielectric response (real and imaginary permittivity) in the 0.1 Hz−1 kHz frequency range, extended voltage response (EVR), and the Shore D hardness test techniques were measured to track aging. The electrical and mechanical parameters have increased monotonically with aging, except the imaginary permittivity, which increased only at frequencies higher than 10 Hz. Furthermore, different quantities were deducted based on the frequency and permittivity data. The electrical parameters and deducted quantities correlation with aging and mechanical parameters were investigated. Since the deducted quantities and the electrical parameters are strongly correlated with absorbed dose and mechanical properties, the electrical measurements can be applied as a non-destructive aging indicator for XLPE/CSPE unshielded low-voltage nuclear power cables.