This PDF file contains the front matter associated with SPIE Proceedings Volume 12488, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

Guided wave testing (GWT) and monitoring are evolving inspection and asset assessment methods that enable indirect measurement of thickness changes. This is a relatively new NDE method that has seen widespread industrial expansion in its use over the last two decades, primarily as an inspection solution for corrosion in pipes and pipelines. Corrosion represents a significant challenge in industrial pipelines and its presence and rate of growth is highly unpredictable and conditional on process and operating environments. This talk will chart the development of the enabling research at Imperial College into Lamb wave testing of pates from 1987 and its outcomes. The development of field useable equipment during the 1990’s allowed for the rapid development of the practical understanding of the complications that result from testing in-service piping. The talk will follow the evolution of the testing methodologies, international codes, standards and training schemes to support the global growth of this new NDE method. Over the last decade a short range quantitative guided wave scanning technique has been patented and recent advances using machine learning to automate the data analysis will also be introduced and discussed.

It is well known that guided ultrasonic waves are suitable to detect damages in composite plates. It has also been shown that these Lamb waves can be utilized to infer material properties through nondestructive measurements. More recently, it was shown that this may be used to determine regional inhomogeneity that is inherent to composite materials due to manufacturing imperfections. In this project, it is investigated whether automated processing of Lamb wave-based data is generally suitable to detect such imperfections. Woven prepreg and short-fiber composite panels are manufactured. A large set of nondestructive measurements are conducted to determine dispersion and attenuation characteristics for multiple regions across each panel. Automated signal processing is performed to extract characteristic features of the signal, which are in turn used to identify any differences within the panels. Moreover, it is studied which type of sensing technology, such as contact transducers, air-coupled transducers or a laser Doppler vibrometer are most suitable for this task. That is, ultrasound measurements with different actuator and sensor combinations are accompanied by additional transducer characterization measurements. Optimal frequency ranges for each transducer are determined in addition to studying potential effects of transducer orientation. Based on all findings, it can be concluded that detecting regional inhomogeneity remains challenging due to various compounding limitations, such as optimal transducer frequency ranges, human error and generally low signal-to-noise ratios in Lamb wave-based measurements, especially at longer propagation distances. In turn, the development of guided wave-based nondestructive evaluation methods require a holistic approach with careful considerations of the employed transducers.

Lightweight, carbon fiber reinforced composites are often selected for aerospace components but are prone to barely visible impact damage, caused by low velocity impacts, during service. Guided-wave-based structural health monitoring (SHM) techniques can efficiently detect impact damage impact in composite structures. However, wave propagation is influenced by material anisotropy resulting in a number of effects. The phase and group velocity of propagating wave modes depend on the wave launching direction, with increased wave speeds in the high stiffness (fiber) directions. Wave energy tends to be focused along the fiber directions, resulting in beam steering or skewing away from the initial wave launching direction. These anisotropic effects, if unaccounted for, could lead to inaccurate localization of damage, and potential regions of the structure where guided waves cannot propagate with sufficient amplitude, reducing damage sensitivity. Wave propagation in an undamaged unidirectional carbon fiber reinforced polymer (CFRP) panel was investigated for the A₀ mode for multiple wave launching directions. Finite Element (FE) modelling was carried out using homogenized anisotropic material properties to investigate the directional dependency of velocity. Point and line sources were modelled to investigate the influence of the excitation source on the guided wave evaluation and signal processing. Wave skewing behavior was visualized for the line source, and wave skew angles and beam spread angles were calculated for a range of propagation angles. Experimental non-contact guided wave measurements were obtained using a laser vibrometer. A PZT strip transducer was developed in order to measure wave skew angles. Experimental and numerical velocities and skew angles were compared with theoretical predictions and good agreement was observed.

The layer-wise manufacturing process, i.e., Additive Manufacturing (AM), of structural components is widely used in various industries, including the aerospace and automotive sectors. This enables the engineers to design a smart structure by providing different materials to different layers based on their required functionality. AM also allows the manufacturing of components with complex geometry. Due to the layer-wise manufacturing, the components are prone to process-induced defects, including inter-layer delamination, cracks, and porosity. These defects could potentially lead to premature failure due to critical loads experienced by the components in service. Therefore, the early detection of these defects is necessary. Various Non-Destructive Inspection (NDI) techniques, such as ultrasonic testing, x-ray tomography, eddy current testing, etc., are conventionally used for defect detection in various industries. The ultrasonic-guided wave-based NDIs are quite popular as they have increased sensitivity to smaller defects and can travel long distances with minimal loss. We have performed the numerical simulation of guided wave propagation in the multi-layered structural waveguide using Fourier transform-based Spectral Finite Element (FSFE) method. The analysis of multi-layered structures is done in two ways, i.e., assuming interface bonding to be perfect (also known as classical laminate theory) and allowing the different levels of interface bonding strength. We have discussed the later case in this paper, in which the interlayer interface bonding layer is replaced by the distributed spring-dashpot systems to represent its viscoelastic behavior. By providing different values to spring and damping constants, we have simulated different levels of interface bonding strength. This problem can be solved via two approaches. In the first approach, the governing differential equations for all the n-layers are solved simultaneously for the dispersion relation. The other approach is based on the transfer matrix method, where the structural waveguide is assumed to be periodic in the length direction with the period as a unit cell. The dispersion relations are obtained from the spectral analysis, and the FSFE is formulated to get the time-domain responses for various excitation. These responses can be used as the base diagnostics signal for inspecting the AM components during manufacturing and in-service.

In this paper results of the damage assessment of composite panel using the guided wave propagation method are presented. Two approaches of elastic wave generation are investigated: (i) contact, piezoceramic transducer (PZT)-based and (ii) non-contact, air-coupled transducer (ACT)-based. Elastic wave sensing is based on scanning laser Doppler vibrometry (SLDV). Both methods of elastic wave generation are compared based on an analysis of elastic wave propagation and damage localization results. For this purpose wave irregularity mapping (WIM) algorithm was utilized. In this research square panels made of fibre reinforced polymer are investigated. Authors investigate artificial damage in the form of Teflon inserts. In this research low-cost and low-frequency (40 kHz) ACT is utilised. The use of the ACT-based wave generation together with the SLDV-based wave sensing give the possibility of realization of the full noncontact damage localization approach. Moreover, authors analysed the acoustic wave generation by the ACT and its propagation in the air using acousto-optic effect together with SLDV measurements.

From airplane wings to overhead power lines, through large blades of wind turbines, a buildup of ice can cause problems ranging from low performance to catastrophic failure. Therefore, it is of the utmost importance to control or prevent ice formation, especially on the critical areas of the structures. However, de-icing and anti-icing countermeasures can result energetically expensive and harmful to the environment. In addition, excessive use thereof will reduce the life of an ice protection system (IPS) and introduce fatigue to the controlled structures. Therefore, in order to manage properly the available resources, it is desirable to have an IPS that can both detect ice formation and monitor the ice thickness on critical surfaces. This would allow the IPS to operate when it is necessary. Ultrasonic guided-wave-based techniques have proved to be reliable for ice detection but approaches to assess ice state over time have not been reported yet. The present work investigates the interaction of ultrasonic waves, propagating in a composite plate, with an ice mass changing state, as it melts. The use of a metric is discussed as indicator of ice condition variation.

A pump-probe ultrasonic laser approach is developed for characterizing the anisotropic photoelasticity (AP) which is related to crystal structures of monocrystalline semiconductors (MSs). The approach exploits the perturbation to the polarization of the monochromatic laser beam when the laser beam interacts with the lattice of MSs. The actively generated strains at the microscopic scale (with a magnitude from 10-4 to 10-5) facilitates detailed, quantitative characterization of lattice properties MSs. A multiphysics model is established to interpret experimental observations, affirming there exists distinct orientation-dependence and crystal-structure-related symmetry of the perturbed polarization state, which is related to mechanical, photoelastic and strain-induced optical anisotropies of MSs.

Pipeline is the main transportation mode of oil and gas resources. However, most pipelines are subject to harsh natural environments, such as external force and temperature fluctuations in addition to the adverse environment. The use of ultrasonic guided wave based nondestructive testing technology for long-term monitoring of the stress in pipeline structure can effectively avoid such disasters. This paper focuses on the propagation characteristics of longitudinal guided waves in pipeline structures subjected to axial stress, to examine the possible applications of various guided wave modes for the stress detection in pipeline structures. Analyzing the generated numerical results, a method for estimating the stress from the velocity variation is proposed. The proposed method has practical applications for pipe stress monitoring using guided waves.

Leveraging modal orthogonality, the normal mode expansion method provides an avenue to extract individual modes in a multi-modal guided wave propagation scenario. The team implements a mode separation technique based on the normal mode expansion and demonstrates its effectiveness in propagating guided waves in an aluminum bar with a rectangular cross-section. Instead of monitoring the wave profiles at the bar end, by the application of mapped field distribution, we can evaluate the amplitude of propagating guided waves at any location. The effectiveness of the proposed technique is verified through numerical models.

Storage tanks are an essential part of petrochemical plants. Most storage tanks are cylindrical, and the base plate is welded at the edge. The bottom plate of the tank is eventually exposed to the environment and soil, leading to corrosion over time. The tank bottom metal loss is one of the defects caused by corrosion initiated by the welded tubular end of the storage tank. Inspecting the storage tank corrosion defect is imperative to avoid undesirable losses. In recent years, the electromagnetic acoustic transducer (EMAT) has become an effective non-contact and non-couplant inspection tool for applying Non-destructive Testing (NDT) & structural health monitoring (SHM). Generally, the guided wave method is preferred to inspect the defect by reflecting propagating wave (SH) modes. It has been observed that the reflected wave modes converse into different modes at a certain reflection angle due to the defect orientations and the welded ends. Due to mode conversion, identifying the defect position with SH wave modes is challenging. This study investigates the influence of mode conversion on the reflected wave from the welded tabular ends of the storage tank. A numerical model has been developed to investigate the SH wave propagation, reflection, and mode conversion due to defects and welded tabular ends. Further, an experiment has been performed with a chevron PPM-EMAT for generating SH wave to validate and compare the simulation results.

In this paper results of detailed analysis of phenomenon of elastic guided wave sensing using fibre optic strain sensors based on Bragg gratings (FBGs) and piezoelectric transducers (PZT) are presented. Attention in this research is focused on possibility of symmetric and antisymmetric guided wave mode sensing with the possibility of mode separation. For this purpose pairs of sensors located on the top and bottom surface is utilized. Moreover, mode index was introduced in order to distinguish fundamental symmetric and anti-symmetric wave modes. Authors compare sensitivity of elastic wave sensing performed by FBG sensor with one based on PZT. Piezoelectric transducers are very popular in applications related to elastic wave generation and sensing in structural health monitoring and on the other hand FBG strain sensors are utilized more and more in elastic wave sensing purpose. Research was conducted for the case of aluminum panel.

This paper presents finite element modeling of an adhesively bonded coupler for the transfer of acoustic modes between two optical fibers. Acoustic modes are propagated through optical fibers for Lamb wave detection with remotely bonded Bragg grating sensors. The model output is compared to previous experimental data, varying the relative diameter of the two fibers. Parameter sweeps of the coupler geometry are also performed to understand how they affect the coupling coefficient.

Optical fiber sensors offer several advantages such as small size ability to be multiplexed, immunity to electric and magnetic fields, etc. In addition, another salient feature of fiber optical grating sensors is the ability to be embedded in the structure. The embedding of fiber Bragg grating (FBG) sensors results in a smaller disruption of the planned function of the structural component and allows the placement of the sensor closer to the area most likely to experience deterioration. The FBG sensors have been commonly used for strain and vibration-based structural health monitoring (SHM) but in the last few years, their use for guided waves (GW) based-SHM has been on a rise. This increasing interest is due to the higher sensitivity for GW sensing achieved through the use of FBG sensors in the edge filtering configuration. This study investigates the use of embedded FBG sensors for GW sensing through the thickness of various structures. The effect of different factors such as the depth of the embedding and material properties on the coupling of the waves into the fiber is investigated using a numerical model and experimental results.

The design of structural health monitoring (SHM) systems usually involves the selection of actuators and sensors, defining their positions on the structure, and post-processing output signals. The definition of the characteristics of the excitation signals before investigating the structural condition is also an important task to be considered to establish a damage detection process. In this context, the present article introduces an approach to determine the optimal parameters to detect symmetric damage in plates when considering perpendicular incidence of flexural waves in the damage. Circular piezoelectric transducers are applied to create and measure the waves. Optimal frequencies to detect the damage, create and measure the flexural waves are observed, which are described in terms of the properties of damage and the piezoelectric transducers, and these frequencies must be close to guarantee good damage detectability. Experimental tests are carried out by considering a rectangular aluminum plate with circular piezoelectric transducers coupled to its surface. Experimental results demonstrate the proposed approach, and the results show that it contributes to establishing more efficient SHM systems.

Local resonances, formed by zero-group velocity (ZGV) and cutoff frequency points, have been extensively studied using impulse-based approaches, such as pulse laser and impact echo. In this work, we showcase the electromechanical impedance (EMI) technique as an option to extract and promote zero-group velocity and cutoff frequency resonances in a waveguide structure. We identify the mechanisms of multiple resonances in the EMI spectra via a wave propagation perspective. Furthermore, we extract the dynamic response profiles at a cutoff frequency and a ZGV frequency to confirm the localized minimum frequency behavior within corresponding branches.

Nonlinear ultrasonic modulation is favored for early-stage fatigue crack detection by inspecting the modulation components at the sum and difference of two distinct input frequencies. However, it is difficult to extract the modulation components using a conventional spectral density function considering the modulation components can be easily buried under noisy environments. In this study, we proposed a new nonlinear ultrasonic analysis method inspired by phase-based motion magnification. First, a one-dimensional time-domain ultrasonic signal is filtered to remove the large motions of the primary linear response components. Then, the filtered signal is used to construct a two-dimensional video and the phase-based motion magnification algorithm is applied to enhance the movements at the predetermined modulation frequencies inside the video. This step is achieved by phase denoising and phase magnification, which supports large magnification factors and is significantly insensitive to noise. Finally, an amplified one-dimensional signal is extracted from the two-dimensional video and can be used for further nonlinear ultrasonic analysis. The proposed method was successfully validated with a group of synthetic data at different noise levels. Additionally, we have also successfully applied the proposed method for fatigue crack detection in a steel padeye.

Recent years have brought more attention to new damage detection approaches based on nonlinear phenomena associated with Shear Horizontal (SH) waves. Many nonlinear effects–previously observed in ultrasonic wave propagation–have been considered for structural damage detection. The major effort has been put on classical nonlinear effects, such as higher harmonic generation. More recently, nonlinear vibro-acoustic modulation and modulation transfer mechanisms have been also observed in SH wave propagation. However, these phenomena have not been used for structural damage detection. The paper attempts to fulfill this gap. The proposed method involves two excitation waves. The low-frequency pumping wave is used for damage perturbation. In addition, high-frequency SH wave is used as a probing wave. The probing wave is modulated by the pumping wave in the presence of structural damage. The method is used in the paper for fatigue crack detection in metallic structural components. The results demonstrate that the proposed approach has a potential for structural damage detection. Previous research work demonstrates that classical nonlinear effects (e.g., higher harmonic generation) observed in SH waves offer better sensitivity to material microdefects than similar effects observed in longitudinal wave propagation. Therefore, it is anticipated that non-classical nonlinear affects associated with SH wave propagation will show similar potential. However, more research work is needed to confirm this assumption.

Various classical and non-classical nonlinear effects have been observed in ultrasonic wave propagation and used for contact-type damage detection. The former relates to higher harmonic generation, whereas the latter is based nonlinear vibro-acoustic modulations effects. More recently both nonlinear effects have been observed in shear horizontal wave propagation. However, the nonlinear crack-wave interaction is still not fully understood. It is assumed that this interaction is enhanced by local nonlinear elasticity and dissipation of elastic waves. The latter effect is the major focus of the paper. Previous experimental research studies demonstrate that high-frequency ultrasonic waves propagation through crack faces that are in contact–and perturbed by low-frequency excitation–exhibit local nonlinear effects of elastic and dissipative nature. The amplitude level of these effects depends on applied stresses. Both nonlinear effects have a great potential for structural damage detection. However, more theoretical and modelling research work is needed to fully understand these non-classical nonlinear effects. Numerical simulations based on nonlinear crack-wave interaction are investigated in the paper. Three models of local nonlinearity are investigated. These are: the Coulomb friction, the nonlinear viscous damping and the hysteretic stress-strain models. Nonlinear wavefield distortions–due to crack-wave interactions–are observed and analyzed. Numerical simulations are performed using the Local Interaction Simulation Approach (LISA), implemented for shear horizontal wave propagation. Wave amplitudes corresponding to generated higher harmonics and modulated sidebands are investigated in the presented work.

A new acoustic source localization (ASL) method that uses the Non-Linear Ultrasonic (NLU) Sideband Peak Count-Index (SPC-I) technique is presented. This method takes advantage of the robustness of the SPC-I technique to predict the location of an acoustic source in an orthotropic composite plate. The proposed method does not require the signal attenuation or time-of-arrival information at the sensors, that other ASL methods need. Since the signal attenuation information is unreliable because different sensors can have different sensitivities due to variations in attachment conditions, a desirable technique should avoid any dependence on the attenuation information. Additionally, no knowledge of the composite plate material mechanical properties is required in this proposed method. This new approach is performed by placing a number of sensors on the composite plate and recording the signals that are generated by the acoustic source. The recorded signals are then processed to obtain the SPC-I value for each sensor. The SPC-I values for each sensor are then used to run through an algorithm that attempts to predict the location of the acoustic source. The composite plate is considered a non-linear material. Therefore, when the signal propagates a longer distance through the plate the recorded signal should show a higher SPC-I value. This phenomenon occurs mainly due to signal scattering and frequency modulation due to material nonlinearity. This phenomenon of increasing SPC-I with propagation distance can be taken advantage of to predict the location of an acoustic source by solely using the non-linear SPC-I parameter. A Carbon Fiber Reinforced (CFR) composite plate with the dimensions of (500 x 500 x1 mm) is used for the acoustic source localization in this manner. Experimental results show that by using this approach, the acoustic source can be predicted at various locations using different excitation signals with reasonable accuracy.

Large civil aircraft structures underlie strict design requirements such as damage tolerance to ensure safety during flight operation. When it comes to the joining of components of such structures, there is still not much confidence in the process of adhesive bonding. Thus, rivets and bolts are commonly used, despite the advent of more diverse material combinations used and the inherent weight penalty of above-mentioned conventional joining techniques in the aviation industry. To overcome the issues of classical structural bonding, the adhesive joint between metal and fiber-reinforced polymers (FRP) can be supported by additively manufactured metallic pins which protrude into the composite material, resulting in so-called pinned hybrid joints. These pins enhance the joint's strength and damage tolerance compared to classical adhesive bonding while being more lightweight than mechanical fastening. However, numerous uncertainties from scattering material properties to sensible manufacturing processes remain. Structural health monitoring (SHM) of pinned hybrid joints may reduce these uncertainties significantly and guarantee the joint's integrity. The present work proposes a new multi-method SHM concept for pinned hybrid joints that applies piezoelectric wafer active sensors (PWAS) and electric contacting of the structure itself for sensing and a partly shared cable network. Thereby enabling various active and passive methods at low cabling effort. Possible methods and their features concerning sensing capability, self-diagnosis, evaluation reliability, installation location, and additional electrical contacting of the monitored structure are discussed with respect to their combined application potential and challenges.

This conference presentation was prepared for SPIE Smart Structures + Nondestructive Evaluation, 2023.

Damage detection in glass-fibre reinforced polymer (GFRP) structures, such as blades of wind turbines, is a challenging task to achieve using most of conventional methods used in Structural Health Monitoring (SHM). The primary cause of this issue is the relatively high internal damping of the material. Vision based methods however circumvent this issue. Among those methods hyperspectral imaging (HSI), a technique in which an image is recorded in a broad spectrum of electromagnetic radiation, has been proven to be a valuable tool for this purpose. Because of the high spectral resolution, hyperspectral images contain information about the chemical composition of the object being scanned. In this study, the chemical data contained in the hyperspectral images of GFRP samples is used as a basis for detection of presence of moisture-related damages. The aim of this study is to develop an algorithm allowing for detection of moisture-related damage in GFRP structures. The algorithm utilizes the interaction of light with moisture through the phenomenon of absorption, cointegration analysis as a denoising and detrending tool, and machine learning methods for the purpose of classification. The results of proposed algorithm are evaluated and its applicability for the purpose of SHM is assessed.

In many situations, real or induced flaws such as tight cracks with known morphology cannot be manufactured in part configuration specimens or in real parts. Typically, fatigue cracks are manufactured in simple geometry specimens such as flat plates, dog-bone shaped flat or round specimens. If a nondestructive evaluation (NDE) technique is required to provide a reliably detectable target flaw size denoted as a_90/95 for induced flaws in real part, then the direct method for qualifying the NDE procedure is to use the appropriate induced flaw specimens and perform probability of detection analysis using these flaws. This can be described as direct POD demonstration testing, which may follow guidelines of MIL-HDBK-1823. This paper considers a case, where induced flaws are not available in part configuration specimens. Therefore, a direct POD demonstration study cannot be undertaken. In such situation, general practice for NDE procedure qualification is to use artificial flaws in simple geometry and part configuration specimens, and induced flaws in the same simple geometry specimens. Signal response data is taken on all sets of artificial and induced flaws. NDE procedure testing on induced flaws in simple geometry specimen is called NDE demonstration testing here. A transfer function NDE procedure qualification method for the forward case calculates predicted induced flaw size for demonstration using a chosen target flaw size. Another transfer function method for the inverse case, calculates the target flaw size using a given demonstration flaw size. The transfer function analysis assumes relationship of artificial flaw signal responses in real parts and simple geometry specimens; and induced flaw responses in simple geometry specimens to induced flaws in real parts. The signal response transfer relationships model should be defined before transfer function models can be devised. Assuming that the signal response transfer relationships model is valid, the forward and inverse case transfer function methods have been devised. Because of lack of signal response data from induced flaws in real part, the 90/95% POD/confidence (P/C) cannot be demonstrated directly. However, the transfer function method may be assessed using simulation to evaluate whether the resulting target flaw size or demonstration flaw size provides adequate confidence to the assumed signal response transfer relationships model. Therefore, the transfer function approach is a risk assessment approach. Both the signal response transfer relationships model and the transfer function model are important in managing risk in results provided by the transfer function NDE technique qualification or assessment. The signal response transfer relationships model needs to be validated with empirical data and then transfer function model needs to be validated for desired P/C on case-by-case basis.

Composite materials are widely used for lightweight design applications due to their beneficial mechanical properties. A crucial aspect to exploit their full potential is to join them efficiently without adding too much additional weight. Especially in aeronautic applications, glued inserts are a common approach to establish contact points with threads. This research is concerned with an advanced design and joining approach for inserts and focuses on static out-of-plane loading as a critical load case for such components. In comparison to classical inserts, no additional adhesive is needed and the weight of the joint is reduced. Two different designs following this approach are developed and tested. The first design is a disc shape, the second one features roots based on a biomimetic approach and both designs are circular symmetric. Damage initiation for both designs starts at a similar load, but the total failure load of the second design is significantly higher than of the first design. For further improvements regarding safety aspects and maintenance time intervals of the joint, a Structural Health Monitoring (SHM) system is implemented. During damage initiation and propagation, strain energy is released and resulting propagating waves can be picked-up on the surface of the laminate using piezoelectric elements. The first Acoustic Emission (AE) events indicate damage initiation. While the first design fails concurrent with the first big AE event, the second design is more suitable for monitoring due to many high peak AE events before its total failure. The results are validated by optical measurements using a Digital Image Correlation (DIC) system.

In this paper, we will discuss the development of 3D printing strategies which enable rapid 3D fabrication of polymer photonic devices and sensors with applications to system health monitoring (SHM). Two-photon polymerization (2PP) 3D printers with 100 nm spatial resolution are commercially available and have enabled the design and fabrication of integrated nano-to microscale polymer photonic devices. Such 3D printing approaches allow us to design truly 3D photonic devices, and this opens the door to fabrication of complex shaped devices that are often produced by methods such as inverse design. Specifically, we report the development of optically active resins that are compatible with two-photon polymerization. We will discuss multiscale 3D printing of photonic devices and sensors with both passive and optically active resins that exhibit both up and down conversion emission when pumped at 980 nm.

The popularity of additive manufacturing has been growing over the last few decades. Additive manufactured composites have a wide range of applications in engineering sectors specifically in aerospace structures. In addition to mechanical loads, they are subjected to thermal loads caused by aerodynamic heating. Temperature increases cause changes in material properties, which complicates thermal stress analysis. The thermal loading was simulated with specific boundary conditions similar to the experiments where the sample was placed inside the oven chamber. While for the mechanical (tensile) testing loading, the sample’s geometry was created with gripping lines to be in accordance with ASTM D3039 standards for tensile tests used in experimental work and surface traction for the applied load. The highest modulus and strength were achieved from the intact sample while the lowest mechanical modulus and strength were obtained in the sample with heat treatment at prolonged temperature of 145^◦C. At high temperatures, matrices soften affecting matrix-dominated properties such as transverse and in-plane shear stiffness and strength. A good correlation between the predictive models and experimental results is obtained.

Additive manufacturing (AM) technology has been used for the creation of complex parts in different industries. The addition of defect detection and load sensing capabilities to these products can highly increase their values. Recently, modern industries have started incorporating AM components into their structures, including those with critical applications like aerospace and civil constructions. This requires the development of accurate and reliable methods for evaluating and monitoring the structural integrity of such components. The Electromechanical Impedance (EMI) method is frequently used to evaluate the health condition of lightweight structures based on the local structural response in the high-frequency range. This study investigates the usage of machine learning (ML) for the health-condition assessment of 3D-printed M3-X plates using EMI conductance (G) and resistance (R) data fusion. Piezoelectric wafers (PZTs) bonded to the center of the plates were used for the measurements. Drilled holes were created and repaired in multiple plates, and several EMI measurements were taken for the healthy, damaged, and repaired states of each plate. After fusing the R and G EMI measurement using a wide frequency range (1 kHz to 5 MHz), principal component analysis (PCA) was employed for feature reduction before a deep-learning approach was applied for diagnosis and damage classification. The findings demonstrate that the EMI method can be applied for the health assessment of AM polymers and is capable of differentiating between their healthy, damaged, and repaired states.

Additive manufacturing (AM) techniques can be applied for the production of carbon fiber reinforced polymer (CFRP) elements. There is a possibility of embedding fiber Bragg grating (FBG) sensors into elements during the manufacturing process. The embedded FBG sensors can be applied for measurements of the element internal temperature and strain. The goal of the paper is to analyze the influence of sub-zero temperatures on the AM CFRP material durability. The measurements were performed on the samples with FBG sensors embedded into the composite during the manufacturing process. It allows to online monitoring of internal strain in the material during its exposition on sub-zero temperatures and mechanical loading. Additionally, the influence of embedded FBG sensors and temperature on the mechanical strength was determined using the tensile tensile test. It was observed that the influence of embedded FBG sensors on the samples structure is neglected. The samples microstructures were also analysed using a scanning electron microscope (SEM). For the purpose of determination of the embedded sensors influence, the achieved results were compared with the results for similar samples without fiber optics. It was observed that exposition of CFRP material on sub-zero temperatures influenced on the microstructure of composite and the mechanical strength of the analysed samples.

Here we report the results obtained for band structure calculations of phononic crystals with rigid scatterers using the plane-wave expansion method. A scatterer with infinite acoustic impedance is modeled by approaching either the mass density or the elastic modulus to infinity. It is shown, that in both cases the dispersion equation contains singular matrices. This singularity leads to the correct band structure in the case of infinite elastic modulus. However, in the limiting case of infinite density the dispersion equation becomes meaningless. We explain the mathematical reason for this drastic difference.

As buildings age, energy begins to leak through various locations such as window seals, walls, subsurface cracks, and damaged areas, even in seemingly healthy structures. Most of the time, areas of energy loss remain undetected because they are not visible to the naked eye. Due to the increasing amount of energy lost through such areas and defects which has an impact on overall energy efficiency. However, infrared images (IR) can be used to detect energy leaks as well as identify subsurface damages. Infrared thermography (IRT) is a popular method for assessing the condition of buildings and infrastructures. While IRT can provide information about the location and severity of energy leaks, manually analyzing the collected data can be a cumbersome process. As a result, there is a need to automate the detection of the areas from where energy is lost. Image segmentation methods based on deep learning algorithms can effectively automate the inspection process. In this study, an approach based on a pre-trained mask region-based convolutional neural network (mask RCNN) is proposed for the first time in conjunction with IR images to localize and quantify areas of heat loss. Mask RCNN demonstrated significant accuracy in identifying the location and quantifying the size of the area of heat loss in inspected buildings with above 99% confidence.

There is an unmet need for a vehicle weigh-in-motion (WIM) technology to detect vehicle overweight and prevent overstress to highway infrastructure. This paper presents a novel computer vision-based non-contact WIM system. The underlining physics is that the force exerted by each tire onto the road is the product of the tire-road contact pressure and contact area. Computer vision is applied (1) to measure the tire deformation parameters so that the tire-roadway contact area can be accurately estimated; and (2) to recognize the marking texts on the tire sidewall so that the manufacturer-recommended tire inflation pressure can be found. In this research, a computer vision system is developed, which is comprised of a camera and computer vision software for measuring tire deformation parameters and recognizing the tire sidewall markings from images of individual tires of a moving vehicle. Computer vision techniques such as edge detection and optical character recognition are applied to enhance the measurement and recognition accuracy. Field experiments were conducted on fully loaded or empty concrete trucks and the truck weights estimated by this novel computer visionbased non-contact WIM system agreed well with the curb weights verified by static weighing. This research has demonstrated a novel application of the computer vision technology to solve a challenging vehicle WIM problem. Requiring no sensor installation on the roadway or the vehicle, this cost-effective non-contact computer vision system has demonstrated a great potential to be implemented.

With a structure’s foundation and supporting ground generally being critical to its design and construction, understanding how soil behaves under various stress and drainage conditions is imperative. It is well known that certain characteristics and behaviors of soils with fines are highly dependent on water content and liquid limit is one of the important soil index properties to define such characteristics. However, conventional liquid limit measurement techniques can be easily affected by the proficiency of the operator, potentially leading to disastrous consequences. The dynamic properties of soils are required in numerous applications, and current testing techniques frequently call for specialized lab equipment, which is often pricy and delicate to test conditions. To address these concerns and advance the state of the art, this study explores a novel method to determine the liquid limit of cohesive soil by employing video-based vibration analysis which may precisely measure and identify the status of a soil’s water content. In this research, the modal characteristics of cohesive soil columns are extracted from videos by phase-based motion estimation. By utilizing the proposed method that analyzes the optical flow in every pixel of the series of frames that effectively represents the motion of corresponding points of the soil specimen, the vibration characteristics of the entire soil specimen could be assessed in a non-contact and non-destructive manner. The experimental investigation results compared with the liquid limit determined by the conventional method verify that the proposed method reliably and straightforwardly identifies the liquid limit of clay. It is envisioned that the proposed approach could be applied to measuring liquid limit of soil in practical field, entertaining its simple implementation that only requires a digital camera or even a smartphone without the need for special equipment or techniques that may be subject to the proficiency of the operator.

Displacement plays a crucial role in structural health monitoring, but the accurate measurement of structural displacement remains a challenging task. Nowadays, some researchers attempt to estimate structural displacement by fusing vision camera and accelerometer measurements. Considering hardware limitations and computational costs, vision measurements are commonly performed at a low sampling rate. Nevertheless, the use of a low sampling rate may cause temporal aliasing in vision measurements, which can cause large displacement errors. In this study, we propose a finite impulse response (FIR) filter-based technique to estimate structural displacement using high-sampling acceleration measurement and low-sampling vision measurement with temporal aliasing. By explicitly eliminating the error induced by temporal aliasing, the displacement estimation accuracy can be significantly improved compared to existing FIR filter-based techniques. The proposed technique was experimentally validated on a single-story building model, and the results show that the displacement estimation performance of the technique was insensitive to the sampling rate of vision measurements. Structural displacement was accurately estimated even when temporal aliasing was present in vision measurements.

A non-contact (vision-based) output-only technique is explored for monitoring and health assessment of civil structures such as multistory buildings and bridges. A consumer-grade camera is used to record the vibrations under natural ambient excitation. Then, the Kanade-Lucas-Tomasi (KLT) algorithm is utilized to track the displacements of desired target points (i.e., the points representing the location of virtual receivers located along the height of the building) and extract the pixeldomain time histories. In the next step, the extracted time history responses are deconvolved between pairs of target points to extract the Impulse Response Functions (IRFs) of each pair. This process, often known as seismic interferometry, extracts the causal and acausal wave propagation behavior between two points of the structure. Depending on the frequency content, this behavior is of interest to reveal structural damage along wave propagation paths. The technique is conceptualized by a numerical example administrated in ABAQUS. It is followed by a preliminary test conducted on a small-scale multistory frame structure subjected to a random shaker excitation, which is monitored by a cellphone’s camera. The main current challenge of this technique is the low frequency of the image recording from the camera. The next phase of this work will utilize a high-speed camera to extract IRFs at frequencies high enough for localized damage detection.

Stereovision systems can extract full-field three-dimensional (3D) displacements of structures by processing the images collected with two synchronized cameras. To obtain accurate measurements, the cameras must be calibrated to account for lens distortion (i.e., intrinsic parameters) and compute the cameras’ relative position and orientation (i.e., extrinsic parameters). Traditionally, calibration is performed by taking photos of a calibration object (e.g., a checkerboard) with the two cameras. Because the calibration object must be similar in size to the targeted structure, measurements on large-scale structures are highly impractical. This research proposes a multi-sensor board with three inertial measurement units and a laser distance meter to compute the extrinsic parameters of a stereovision system and streamline the calibration procedure. In this paper, the performances of the proposed sensor-based calibration are compared with the accuracy of the traditional image-based calibration procedure. Laboratory experiments show that cameras calibrated with the multi-sensor board measure displacements with 95% accuracy compared to displacements obtained from cameras calibrated with the traditional procedure. The results of this study indicate that the sensor-based approach can increase the applicability of 3D digital image correlation measurements to large-scale structures while reducing the time and complexity of the calibration.

Current trends in polymer fiber production for nonwoven material applications focus on increasing production rates and decreasing the fiber thicknesses. The quality of the polymer fibers during the fiber spinning process is influenced by the processing parameters, such as the spinning speed, throughput, and the polymer material. Irregularities in the crystallization process during the extrusion of the fibers can lead to stress concentrations and defects in the fibers that could cause failure of fibers and potential failure of the nonwoven material that is manufactured from those fibers. The ability to recognize these irregularities in fibers using a non-destructive measurement method would reduce the downtimes for production lines as well as provide in-situ quantitative data that could be used for optimization of the production process parameters. In this study, we implemented a high-speed polarization imaging technique that is capable of non-destructive measurement of the internal stress fields as well as detection of defects within a post-fabricated fiber. This imaging technique has been combined with a motion tracking algorithm for accurate alignment of the images corresponding to the same segments of the fiber. The results show that the technique is capable of detecting stress concentration regions in fabricated fibers in static and dynamic testing conditions. The sensitivity of the system also allows to track the changes in the distribution of the internal stress fields in static and dynamic loading. Future studies will apply the technique to the fiber spinning process.

This paper describes a monitoring/inspection technique for the estimation of longitudinal stress in continuous welded rails (CWR) to infer the rail neutral temperature (RNT), i.e. the temperature at which the net longitudinal force in the rail is zero. The technique is based on the use of vibration measurements and machine learning (ML). A finite element analysis is conducted to model the relationship between the boundary conditions and the longitudinal stress of any given CWR to the vibration characteristics of the rail. The results of the numerical analysis are used to train a ML algorithm that is then tested using field data obtained by an array of accelerometers polled on the track of interest. In the study presented in this article, the proposed technique was tested in the field. A commercial FEM software was used to model the rail track as a short rail segment repeated indefinitely and under varying boundary conditions and stress. Three ML models were developed using hyperparameter search optimization techniques and k-fold cross validation to infer the stress or the RNT the frequencies of vibration extracted from the time waveforms obtained from two accelerometers temporarily attached to the rail. The results of the experiments demonstrated that the success of the technique is dependent on the accuracy of the model and the ability to properly label the modes of the detected frequencies. The ML was also able to learn from the experimental data only and successfully predicted the neutral temperature of the tested rail section

In this study, we develop an end-to-end deep learning-based inverse design approach to determine the scatterer shape necessary to achieve a target acoustic field. This approach integrates non-uniform rational B-spline (NURBS) into a convolutional autoencoder (CAE) architecture while concurrently leveraging (in a weak sense) the governing physics of the acoustic problem. By utilizing prior physical knowledge and NURBS parameterization to regularize the ill-posed inverse problem, this method does not require enforcing any geometric constraint on the inverse design space, hence allowing the determination of scatterers with potentially any arbitrary shape (within the set allowed by NURBS). A numerical study is presented to showcase the ability of this approach to identify physically-consistent scatterer shapes capable of producing user-defined acoustic fields.

This work presents acoustic emission (AE) waveform to source coordinates transformation at hollow cylinders facilitated by multiple Lamb mode arrivals due to the cylindrical geometry. Variational autoencoder (VAE) is selected to perform waveform source discrimination by capturing the delays in time-of-flights (TOF) between modes described in the transformation. An AE waveform dataset simulated by pencil lead break on a liquid nitrogen tank was collected to validate the proposed approach. The result indicates that VAE is capable of separate AE waveforms by their sources through the targeted delays between mode arrivals.

Recent machine learning (ML) algorithms have resulted in new paradigms for extracting spatio-temporal characteristics (STCs), such as frequency spectra of critical infrastructures and performing structural health monitoring. However, the accuracy of the STCs extracted using ML is affected by any noise in the data used to train and test the ML algorithms. While noise reduction methods have been successfully proposed, they are application-specific, and none consider the dynamics of the targeted system. Hence, a novel framework named time-inferred autoencoder (TIA) is proposed. The TIA is based on a long, short-term memory (LSTM) neural network to learn the dynamics of the system and a maximum correntropy loss function for noise removal. The robustness of the TIA is validated by collecting a video of an undamaged beam for training the framework and learning the structure’s STCs. Later, the capability of the trained TIA to adapt to changes in the system’s dynamics and reconstruct the STCs is validated by recording noise-corrupted videos of a beam in three different damaged configurations. Results of laboratory tests showed that the TIA reconstructs the natural frequencies of the structure with an error of less than 1%. If further developed, the proposed framework can be used as a structural dynamics tool given its robustness and capability to adapt to changes and noise in the system.

Performances are a key concern in aerospace vehicles, requiring safer structures with as little consumption as possible. Composite materials replaced aluminum alloys even in primary structures to achieve higher performances with lighter components. However, random events such as low-velocity impacts may induce damages that are typically more dangerous and mostly not visible than in metals. Structural health monitoring deals mainly with sensorised structures providing signals related to their “health status” aiming at lower maintenance costs and weights of aircrafts. Much effort has been spent during last years on analysis techniques for evaluating metrics correlated to damages’ existence, location and extensions from signals provided by the sensors networks. Deep learning techniques can be a very powerful instrument for signals patterns reconstruction and selection but require the availability of consistent amount of both healthy and damaged structural configuration experimental datasets, with high materials and testing costs, or data reproduced by validated numerical simulations. Within this work will be presented two supervised deep neural networks trained by experimental measurements as well as numerically generated strain propagation signals. The final scope is the detection of delaminations into composites plates for aerospace employ. The first type is based directly on the processing trough a convolutional autoencoder of the rough signals of both healthy and damaged structural configurations. The second approach is instead based on the production of images trough signal processing techniques and on employ of an image recognition convolutional network. Both networks are trained and tested on combinations of experimental and numerical data.

Ultrasound is often favored in biopsy guidance since it is non-ionizing, inexpensive, portable, and has a high frame rate. However, imaging probes that operate at a low frequency may not be able to differentiate between tiny targets and surrounding tissues clearly, and at a high frequency, it suffers from tissue scattering and signals attenuation, which is hard to image deeper targets such as lung tissues. In this study, we developed a biopsy needle (with a size of 18 G) integrated with a 30 MHz high-frequency ultrasound transducer (axial resolution: ~ 100 µm) for the lung nodule biopsy in vitro test. To mimic contrasting biological tissues, a melamine foam-gelatin phantom was developed. With an advancing step of 0.5 mm, the distance from the biopsy needle to the gelatin-foam boundary was estimated by the speed of sound in gelatin and the time-of-flight of the echo signal. The results showed that the 30 MHz ultrasound transducer can map the geometry of the gelatin-foam boundary, indicating the capability of distinguishing tumor and healthy lung tissue with this ultrasound-guided biopsy technique.

Respiratory rate is a vital sign that signifies the movement of air into and out of the lungs through ventilation. In modern medical practices, this vital sign is often inaccurately measured by hospital staff [1]. This phenomenon is problematic considering a fluctuation in respiratory rate is often observed as an early indicator of illness. The recent COVID-19 pandemic resulted in an influx of patients visiting hospitals and a greater need for hospital staff to monitor the vitals of patients in case of respiratory distress. To aid in the important measuring of patient vitals, a device was created to non-invasively measure respiratory rate. This portable, cost-effective device utilizes thermal analysis and facial recognition through two cameras to measure and calculate a patient's respiratory rate from a distance of 30-60 cm away. The respiratory rate is then displayed via a smartphone application. A group of subjects participated in a testing procedure to determine the accuracy of the device. Subject respiration was recorded by the device over a period of 30 seconds. The results were compared to a manual count, verified by a real-time heart rate monitor. The device’s algorithm was found to have an increase in accuracy compared to a previous study of respiratory rates manually counted by trained medical staff [2]. This device, the LeTourneau Engineering Vital Imaging System (LEVIS), provides a more accurate measurement of respiratory rate, which can enable medical staff to attend to other tasks during the measurement period.

This conference presentation was prepared for SPIE Smart Structures + Nondestructive Evaluation, 2023.

Patients recuperating from orthopedic surgery require frequent monitoring and hospital visits with a wealth of personal medical data generated both on and off-site, making it challenging to maintain records. This paper discusses a secure blockchain-based data management software to enable safe remote access without compromising patient information. The BlockTrack software developed at our group will be customized to interface with modules for orthopedic recuperation monitoring. Modules can consist of ultrasonic bone health monitoring sensors, connected to relay nodes that can transmit patient data to the BlockTrack mobile app, which then intercepts the information to be stored securely on a cloud-based Blockchain network. Each record will have a unique ID enabled by Blockchain, for secure access and review of patient information by other parties, including doctors and pharmacists. Key findings are discussed with a goal to further develop this solution.

The recent decade has seen aging bridge infrastructure, especially in developed countries, at an unprecedented rate. Their scheduled maintenance and detection of gradual damages are challenging with decreasing number of skilled engineers. To solve these problems, we aim to investigate the possibility of bridge monitoring by distributed acoustic sensing (DAS) system, which uses optical fiber cables to sense surrounding vibrations. Most conventional monitoring methods, such as accelerometer-based point sensors, require a dedicated location for installation on bridges. However, DAS can obtain bridge vibration responses through Rayleigh backscattered light from optical fibers already laid for communication purposes without attaching to a dedicated location for bridge monitoring. The multiple measurement positions of one bridge are possible by taking advantage of optical fiber as a line sensor, hence expanding the monitoring of multiple bridges simultaneously over the road infrastructure. This study also discusses the statistical characteristics of bridge vibration responses measured by the DAS system. We experimented on a model bridge to evaluate the sensing performance and detection capability of the damaged condition, where the damaged condition represents the three saw cuts on both sides of the web plates of the model bridge. The experiment results evaluated from analysis of variance using the F-test show that frequency responses of DAS in both intact and damaged bridge conditions matched those of accelerometers used as a reference in this study. Our results suggest that DAS can be applicable to bridge damage detection like accelerometers.

Presently, railroad monitoring strategies focus on preventative maintenance by detecting wheel anomalies using wayside detection methods (e.g., wheel-impact load detection), and direct detection of track anomalies using onboard systems (e.g., track geometry vehicles). Both approaches are periodic, manual, and do not support real-time track damage detection. Recent research has focused on detecting damage from acceleration signals obtained onboard moving vehicles and identifying anomalies from derived structural dynamic properties. Though promising due to inherent scalability and cost efficiency, its main goal is to detect damage on the supporting infrastructure and has never before been tested for detecting rail crack damage. Among other reasons, a robust anomaly detection algorithm is missing to allow the industry to embrace an automated and more cost-effective monitoring technique. In this work, we leverage a lab-scale track and moving vehicle actuation system that is scaled with the assistance of industry experts, and comprises a vehicle instrumented with two onboard vertical accelerometers. Cracked rails are simulated by introducing discontinuities (longitudinally and transversely). Several types of feature extraction and dimensionality reduction techniques are employed to evaluate their ability to separate damaged and undamaged records. Inspired from previous work, this work tests the ability of existing data-driven damage detection algorithms to detect local damage by using a novel super modular, precise, and realistically scaled down version of a train-track system. The results of the damage sensitivity show that principal component analysis has the highest balanced combination of recall and true negative rate, compared to other techniques.

Damage identification using non-destructive techniques can aid in structural health monitoring and rehabilitation applications, especially in common construction materials like concrete. This paper attempts to provide a proof-of-concept, for the application of a novel feature extraction algorithm based on Uniform Manifold Approximation and Projection (UMAP), to detect highly common strength impacting defects such as cracking in concrete. To this aim, ultrasonic testing is adopted to transmit pressure waves through discretised locations along the specimen. The transmitted time signals are received by 54 kHz receivers and analysed with UMAP, resulting in highly reliable data separation between healthy and damaged sections–showing better results compared to competing wavelet decomposition frameworks. The key contribution of the study lies in the application of UMAP, for the first time, for damage detection in unreinforced concrete. This proof-of-concept demonstration with a positive outcome can lead to future investigations, which may delve deeper into the algorithm’s efficacy with various other defect types within concrete and reinforced concrete.

Structural health monitoring methods are in general based on material testing during operation of structures under monitoring. Small punch test method is a new approach that allows for material sampling on metal structures without the need to repair the damage due to the size and shape of material volume taken from the structure. The way of material sampling for SPT makes it a non-destructive testing method thus allowing material condition assessment at any stage of machine life without the need to exclude it from usage and cause production downtime. Small punch test results provide yield stress and ultimate strength. In this work we investigate the accuracy of the SPT method in comparison with standard, widely used in industry uniaxial tensile test that is destructive but regarded as a default source of precise mechanical properties data. Materials used for this research are boiler steels in various conditions starting at unused materials and ending on heavily fatigued samples from retired pressure tanks. The goal of this study is to establish a direct correlation between SPT and UTT for investigated materials by the means of statistical analysis of the results. Cluster analysis has been used to investigate the influence of small thickness differences between samples on final results in comparison to the data obtained from standard material testing methods by mixing the data together.

In order to support the continued trend of increased use of composite materials in many industries, efficient testing systems need to be developed. That is, existing visual inspection techniques may be inadequate as they do not allow for the detection of internal flaws such as delaminations. The contributions of this paper to the nondestructive testing (NDT) community are threefold: it provides 1) an overview of an opportunity for undergraduate engineering education in form of a class project, 2) a description and demonstration of a newly developed multi-axis positioning system for air-coupled transducers and 3) the application to the characterization of composites. A competition-based course project was designed for an undergraduate machine design class. The objective of the project was to design a low-cost, multi-axis positioning system for NDT experiments. The winning design was built and features an innovative robotic arm with many custom-made components, including a 2-D goniometric stage for orienting air-coupled ultrasound transducers. The system allows for automated and accurate positioning to acquire detailed wave propagation and scattering data from NDT experiments on composite specimens. The NDT positioning system’s capabilities are demonstrated via dispersion characterization problems on metal and composite specimens. Specifically, a pitch-catch methodology is employed where one stationary transducer is complemented by a roving transducer positioned with the robotic arm. Several datasets are collected and different signal processing techniques are employed in an effort to characterize the studied specimens. The results are compared to the existing literature and simulation data, showing good agreement.

In this study, an adaptive scheme for autonomous underwater vehicle systems is developed that utilizes a model of the complex nonlinear dynamics and control of the vehicle to enable detection of sensor faults and failures. Our framework for design of fault identification and risk management, incorporates a neural network-based nonlinear observer to monitor the input and output of the control system for detection of a variety of faults in the sensors. The training occurs online and parameters of the recurrent neural network are updated by an extended Kalman filter. The fault detection and identification system was developed and integrated for a nonlinear model of a Remus-100 underwater vehicle. The results obtained from the numerical simulation shows the system's ability for prompt detection and isolation of a variety of sensor faults. Further study is needed for development of experimental validation and verification and computational efficiency of the proposed algorithm.

Periodic pigging of pipelines is essential for the inspection and maintenance of the gas pipeline network. Undetected cracks can be detrimental to pipelines and can often compromise the integrity of the pipeline. Pigging operation requires the pipeline inspection gauges to move at a moderately low yet uniform speed to inspect the defects, including corrosion, cracks, and deposits, developed in the pipeline after prolonged service. The speed of the pipe health monitoring robot (PHMR) can attain an undesirable high magnitude due to fluctuations in pressurized gas flow conditions prevailing in the pipelines. The high travel speed results in aliasing, leading to a consistent sampling of error-prone inspection data. The present study explores and expands on the previous speed control units by developing an innovative method of a novel speed control system based on the combination of deflector bypass flow and hydraulic brake mechanisms and experimentally validating it for PHMR. The speed control system developed is highly responsive to the changes in the speed of the PHMR since the incompressible nature of the brake fluid makes instantaneous transmission of pressure changes for the braking action possible. The modular nature of the developed speed control system enables it to be attached to any wheel suspension assembly-based PHMR and has been reported to passively regulate any undesirable high-speed spikes maximum by 51% within the acceptable range. The system is operated without a power supply, making it highly safe while operating in inflammable gas pipelines and a cost-effective and reliable solution that can help in accurate, effective, and seamless inspection of the gas pipelines spread over a large area of the pipeline network.

The article presents an approach to developing a novel algorithm for effective Local Defect Resonance (LDR) detection to improve the quality and automate structural integrity assessment. The proposed technique is applied to samples with different sizes of damage. The research is conducted in parallel with virtual and experimental models. The sample was instrumented with a surface-bonded low-profile piezoceramic transducer to excite the structure. Scanning laser vibrometry was used to obtain response signals from the plate. The frequency characteristics were determined for individual points in the structure based on the response signal and the known excitation. The algorithm uses the amplitude difference between the local damage area and the entire structure as a background. On this basis, local defect resonance parameters are determined.

Among various signal processing approaches, stochastic resonance (SR) has been widely employed for weak signal detection and mechanical fault diagnosis. Various advancements have been focused on identifying useful information from the frequency domain by optimizing parameters in a post-processing environment to activate SR. Yet, these methods often require detailed information about the original signal a priori, which is challenging from measurements that are already overwhelmed by noise. Furthermore, classical bistable SR has often been employed for weak signal detection, which exhibits an inherent signal distortion due to output saturation that reduces the signal recovery performance. To address these concerns and advance the state of the art, we propose a novel signal denoising method that exploits unsaturated SR in a parallel array of piecewise continuous bistable systems. The original noise-contaminated signal is adaptively scaled by an optimal gain value that is determined from a non-dimensional model based on the attendant noise level, which is one of the few parameters that can be reliably identified from practical noise-contaminated signals. As a result, the proposed approach can operate without any post-processing optimization and parameter selection. Numerical investigations are performed with a simulated acoustic emission signal (amplitude modulated sine pulse) with various amplitudes and attendant noise levels to illustrate the operation principle and the effectiveness of the proposed approach. The results exemplify the promising potential of implementing the proposed approach for enhancing online signal denoising in practice.

Guided waves (GWs) are very popular for the damage detection of thin-walled structures. They propagate large distances and relatively few may be used for damage localization. The problem with their use for complex structures is the signal processing. Due to the presence of multiple modes, and mode conversion and reflections from the structure boundaries and discontinuities the signal processing is indeed challenging. In order to reduce the complexity, lower frequencies are used to limit the excitation only to the fundamental modes. Even then the signal processing may be challenging. So efforts are focussed on the ability of some sensors to detect only a particular wave. This paper aims at investigating the suitability of the polarization maintaining FBG (PM-FBG) for this purpose.

Fatigue crack growth is one of the most common damage types in aluminum components, widely used in aircraft structures. Detection of fatigue cracks at an early stage is important to guarantee aircraft safety. Efficient non-destructive evaluation (NDE) and structural health monitoring (SHM) can be achieved by employing low frequency guided ultrasonic waves, as they can propagate long distances along plate structures. SHM systems using distributed guided waves sensors have been proposed for efficient monitoring, but have limitations due to environmental influences such as the temperature stability of the conventional baseline subtraction method. The scattering and mode conversion of guided waves at part-thickness defects was investigated to quantify the sensitivity for defect detection and the potential for the development of a baseline-free SHM methodology employing mode converted guided waves. Baseline-free SHM methodology employing mode conversion is expected to overcome some of the limitations caused by environmental factors and to improve sensitivity and stability by employing new or modified signal processing algorithms. A three dimensional (3D) Finite Element (FE) model was developed to predict the mode conversion of the fundamental guided wave modes. The influence of defect length and depth on detection results were investigated numerically. The detection sensitivity for part-thickness defects in a plate is quantified.

Total ankle replacement (TAR) is the main clinical treatment for end-stage ankle arthritis, replacing the ankle joint with a metallic implant. Component loosening, fracture, and wear are the main reasons for implant failure, requiring revision surgery. A non-invasive guided wave monitoring technique is being developed to ultimately evaluate in-vivo implant device integrity and bone-implant interface conditions (osseointegration). Finite Element (FE) simulations were performed to investigate the feasibility and sensitivity of ultrasonic monitoring of the interface conditions, assessing suitable guide d wave modes and excitation frequencies. A simplified implant geometry was developed for FE modelling in Abaqus/Explicit. Selected guided wave modes (higher-order longitudinal modes sensitive to bone/implant interface changes) were excited at the distal end of the metallic implant component for detection of variations of bone-implant contact conditions. Simulation results showed the feasibility for guided ultrasonic waves to monitor bone implant osseointegration. Guided wave signal amplitude and changes of arrival time of pulses propagating along the metallic implant can indicate the presence of improved osseointegration. The potential for the integration of the bone implant monitoring sensors and other biosensors into secure, blockchain-based, remote patient data management systems will be further investigated.

The remote bonding configuration allows the application of FBG sensors in extreme environments. But as the length of the fiber increases the attenuation in the fiber becomes significant and the signal-to-noise ratio (SNR) is reduced. The amplitude of wave energy coupled in the fiber depends on the type and quality of the bond, the materials in contact as well as the attenuation of the wave in the fiber. In this paper, FBG sensors with different coatings are used and the amplitude of the wave coupled is studied in each of the cases. Four different material coatings including acrylate, polyimide, aluminium coated and PEEK-coated FBGs are used for the purpose.