Concrete structures are being subjected to increasing loads and used beyond their intended lifespan. When combined with operators shrinking maintenance budgets it is imperative structures are monitored for damage. Acoustic Emission (AE) and AE Tomography offer a method for damage detection and characterisation. However, they are encumbered by their equipment and setup requirements, and best used on areas where damage is already known to have occurred. An autonomous pulse catch system could be used to identify these areas. To explore the potential of such a system, seven concrete beams differentiated by their aggregate size were tested using an automated pulse and receive system. The effect of aggregate size, and pulse frequency on attenuation and wavespeed are investigated. The pulse regime consisted of narrowband pulses ranging from 30 kHz to 500 kHz. The results show that high frequency pulses experienced greater attenuation than low frequency pulses. Whilst a link appears between the size of the aggregate and the rate of attenuation of high frequency signals. A strong negative correlation between amplitude loss and pulse frequency was observed. For the specimen containing coarse aggregate, pulse velocity was constant between 100 kHz and 500 kHz, whereas specimens containing coarse aggregate experienced frequency dependant attenuation. This testing shows the significant potential for an automated pulse and receive system, which could lead to the development of an autonomous health monitoring system. In addition, the approach can be instrumental in developing large data sets, that can be used in Machine Learning processes, to develop a deeper understanding of AE signal propagation in concrete materials.

The demand for flexible large area optoelectronic devices such as organic light-emitting diodes (OLEDs) and organic photovoltaics (OPVs) is growing. Roll-to-roll (R2R) printing enables cost-efficient industrial production of optoelectronic devices. The performance of electronic devices may significantly suffer from local electrical defects. The dark lock-in infrared thermography (DLIT) method is an effective non-destructive testing (NDT) tool to identify such defects as hot spots. In this study, a DLIT inspection system was applied to visualize the defects of R2R printed silver conductors on flexible plastic substrates. A two-stage automated defect recognition (ADR) methodology was proposed to detect and localize two types of typical electrical defects, which are caused by complete or partial breaks on the printed conductive wires, based on localized segmentation and thresholding methods.

One of the most significant benefits of Acoustic Emission (AE) testing over other Non-Destructive Evaluation (NDE) techniques lies in its damage location capability over a wide area. The delta-T mapping technique developed by researchers has been shown to enable AE source location to a high level of accuracy in complex structures. However, the time-consuming and laborious data training process of the delta-T mapping technique has prevented this technique from large-scale application on large complex structures. In order to solve this problem, a Finite Element (FE) method was applied to model training data for localization of experimental AE events on a complex plate. Firstly, the FE model was validated through demonstrating consistency between simulated data and the experimental data in the study of Hsu-Nielsen (H-N) sources on a simple plate. Then, the FE model with the same parameters was applied to a planar location problem on a complex plate. It has been demonstrated that FE generated delta-T mapping data can achieve a reasonable degree of source location accuracy with an average error of 3.88 mm whilst decreasing the time and effort required for manually collecting and processing the training data.

Permanently installed sensors are a cost-effective solution for corrosion monitoring due to their advantages, such as less human interference and continuous data acquisition. Some of the most widely used permanently installed corrosion sensors are ultrasonic thickness (UT) gauges. However, UT sensors are limited by the need for coupling agents between pipe surfaces and sensors. The magnetic eddy current (MEC) method, on the other hand, does not require couplant and can be used over insulations. With the development of powerful rare earth magnets, MEC sensors with low power consumption are possible, and there is the prospect of using them as permanently installed sensors. A novel wireless magnetic eddy current sensor has been designed and optimized using finite element simulation. Sensitivity studies of the sensors reveal that the excitation frequency is a critical parameter for the detection of corrosion defects. An in-depth explanation of the relationship between the sensitivity of the sensor and the excitation frequency is presented in this paper. The results of an accelerated corrosion test, conducted to simulate the service environment of the sensor, are also discussed. It was observed that the sensor signals are very sensitive to corrosion defects and show no subtle differences due to temperature and humidity changes.

The detection capability of magnetic eddy current and magnetic flux leakage sensors depends on the magnetization level in the test specimen. While low magnetization field intensity makes it difficult to detect defects, higher magnetization levels increase background noise as well as the size and weight of the sensors. Moreover, powerful magnets are used in the magnetization circuit that is difficult to handle and pose potential health and safety hazards. Finite element modelling is widely used for the optimization of the design of magnetization yokes. Modelling softwares are limited in their ability to conduct artificial intelligence-based optimization and require a large number of iterations. This can be time-consuming and computationally expensive. An optimization technique using particle swarm optimization algorithm for designing the excitation system for magnetic eddy current sensors is presented in this paper. Numerical simulation is used to determine the objective function and input variables for the algorithm. A comparative study is carried out to evaluate the algorithm's performance against genetic and artificial bee colony algorithms. The sensor design parameters obtained using the algorithm results are validated through experiments. The results show that the PSO is a fast and computationally efficient algorithm for optimizing the yoke design.

Railway axles are safety-critical components, which are designed to be safe and reliable under standard operation. However, unexpected service conditions, impact events and issues missed in maintenance can affect their lifetime. Non-destructive testing (NDT) methods employed to assure structural integrity are expensive and disruptive requiring axles to be taken out of service for inspection. Therefore, there is need for more efficient, real-time monitoring approach. This paper presents an overview of key challenges facing successful in-service implementation of an acoustic emission (AE) condition monitoring system. This is followed by an experimental study of AE data captured during a mechanical test of a railway axle. The data is clustered using a self-organising map (SOM) to differentiate damage signals from other sources. The results presented show consistency with the crack growth measured using traditional NDT techniques.

Shearography, as a novel non-destructive evaluation technique, has shown notable ability in the detection of composite materials. However, in current shearography practices, the phase shifting and loading methods applied are mainly static. For instance, vacuum hood or force loading facilities are often used in phase-shifting shearography, and these are hard to realise with robotic control, especially for on-board inspection. In this study, a dynamic process for detecting defects in the subsurface of a wind turbine blade (WTB) using spatial phase shift with dynamic thermal loading was proposed. The WTB sample underwent a dynamic thermal loading operation, and its status is captured by a Michelson interferometric-based spatial phase shift digital shearography system using a pixelated micro-polarisation array sensor. The captured images were analysed in a 2D frequency domain and low-pass filtered for phase map acquisition. The initial phase maps underwent a window Fourier filtering process and were integrated to produce a video sequence for realisation of visualising the first derivative of the displacement in the process of thermal loading. The approach was tested in experimental settings and the results obtained were presented and discussed. A comparative assessment of the approach with shearography fringe pattern analysis and temporal phase shift technique is also presented and discussed.

Hard ballistic body armour plates are designed to withstand the impact of a bullet and protect the wearer, if this happens the armour is clearly damaged and so is retired from service. Mishandling, however, such as dropping the armour, may cause minor and difficult to detect damage which compromises the effectiveness of the plate. Current methods of inspection involve shipping the plates to a central location, performing a thorough inspection and returning them to service if uncompromised; this is costly and requires redundancy of equipment for when not in service. Acousto-Ultrasonics is a method of structural health monitoring in which ultrasonic waves are excited in a structure by a transducer and receivers record the response, any deviation from a baseline measurement give an indication of damage within the structure. Within this paper the development and testing of a novel handheld prototype device is presented, which gives a simple yes/no answer to if there is damage on the plate. This inspection is quick and easy to perform by unskilled personnel. Low profile sensors have been utilised combined with a novel flexible circuitry with built in memory, which does not compromise the effectiveness of the armour.

Aircraft structural damage detection is becoming of increased importance. Technologies such as acousto-ultrasonic have been suggested for this application; however, an optimization strategy for sensor network design is required to ensure a high detection probability while minimizing sensor network mass. A methodology for optimizing acousto-ultrasonic transducer placement for adhesive disbond detection on metallic aerospace structures is presented. Experimental data sets were acquired using three-dimensional scanning laser vibrometry enabling in-plane and out-of-plane Lamb wave components to be considered. This approach employs a novel multi-sensor site strategy which is difficult to achieve with physical transducers. Different excitation frequencies and source–damage–sensor paths were considered. A fitness assessment criterion which compared baseline and damaged data sets using cross-correlation coefficients was developed empirically. Efficient sensor network optimization was achieved using a bespoke genetic algorithm for different network sizes with the effectiveness assessed and discussed. A comparable numerical data set was also produced using the local interaction simulation approach and optimized using the same methodology. Comparable results with those of the experimental data set indicated a good agreement. As such, the numerical approach demonstrates that acousto-ultrasonic sensor networks can be optimized using simulation (with some further refinement) during an aircraft design phase, being a useful tool to sensor network designers.

As worldwide wind energy generation capacity grows, there is an increasing demand to ensure structural integrity of the turbine blades to maintain efficient and safe energy generation. Currently, traditional non-destructive testing methods and visual inspections are employed which require the turbine to be out-of-operation during the inspection periods, resulting in costly and lengthy downtime. This study experimentally investigates the potential for using Lamb waves to monitor the structural integrity of a composite wind turbine blade that has been subject to an impact representative of damage which occurs in service. 3D scanning laser vibrometry was used to measure Lamb waves excited at three different frequencies both prior to, and after, impact to identify settings for an optimal system. Signal processing techniques were applied to the datasets to successfully locate the damage and highlight regions on the structure where the Lamb wave was significantly influenced by the presence of the impact damage. Damage size resulting from the impact was found to correlate well with the laser vibrometry results. The study concluded that acousto-ultrasonic-based structural health monitoring systems have great potential for monitoring the structural integrity of wind turbine blades.

This paper presents a novel numerical technique that combines predictions of impact-induced damage and subsequent ultrasonic guided-wave propagation in composite laminates, with emphasis on the development and verification of the modelling framework. Delamination and matrix cracking are considered in the modelling technique, which is validated by experimental measurements on a carbon-fibre/epoxy plate using a drop-weight impact tower and a scanning laser vibrometer. Good agreement has been found between simulations and experiments regarding the impact response and global-local wavefields. Effects of these two damage modes, damage extent and multiple impacts on guided waves are studied using the modelling tool. Matrix cracking leads to lower wavefield scattering compared with delamination, particularly in un-damaged regions. The modelling strategy can provide valuable guidelines for optimising health-monitoring arrangements on composite structures that are susceptible to impacts, and the guided-wave model can also be integrated with other numerical models for predicting internal flaws in composite laminates.

Acoustic Emission (AE) is a promising technique for Structural Health Monitoring applications (SHM) and involves detecting the ultrasonic stress waves generated by damage initiation growth in a structure. One advantage of AE for SHM is the ability to locate AE sources. Delta-t mapping was developed to overcome the limitations of the conventional AE location technique. The disadvantage of the delta-t mapping is that it requires the manual collection of experimental training data. This paper explores the possibility of using local interaction simulation approach (LISA) to generate simulated training data for the algorithm. The results for locating a fatigue crack are presented for both the experimental and simulated training data and showed average errors in source location of 3mm and 8mm respectively. This demonstrates the potential of using simulated training data for the mapping technique which would ultimately reduce implementation of delta-t mapping on large scale structures.

Historical trends show that the number of commercial aircraft will double in the next fifteen years. With the increasing complexity of aircraft structures and materials there is an essential need to continually monitor the structure for damage. This task is difficult to achieve using traditional non-destructive testing (NDT) methods alone. It is therefore proposed that a structural health monitoring (SHM) system should be implemented for the continual monitoring of the structure. It is forecast that the implementation of such a system will result in increased safety, cost and time savings in maintenance as well as mass savings of up to 15% in the aerostructure. There has been a reluctance within the aerospace industry to adopt acoustic emission (AE) techniques based on previous experiences. One of the main issues associated with the failure of AE techniques to detect damage can be attributed to poor considerations for sensor placement. Although there have been many studies conducted with the aim of optimising sensor placement for SHM applications there are few published works with the specific aim of optimising an AE sensor network. This is most likely because AE is passive technique which makes an experimental investigation difficult.

There are many advantages to adhesively bonding stiffeners onto aircraft structures rather than using traditional mechanical fastening methods. However there is a lack of confidence of the structural integrity of adhesively bonded joints over time. Acousto-ultrasonic Lamb waves have shown great potential in structural health monitoring applications in both metallic and composite structures. This paper presents an experimental investigation of the use of acousto-ultrasonic Lamb waves for the monitoring of adhesively bonded joints in metallic structures using 3D scanning laser vibrometry. Two stiffened panels were manufactured, one with an intentional disbonded region. Lamb wave interaction with the healthy and disbonded stiffeners was investigated at three excitation frequencies. A windowed root-mean-squared technique was applied to quantify where Lamb wave energy was reflected, attenuated and transmitted across the structure enabling the size and shape of the defect to be visualised which was verified by traditional ultrasonic inspection techniques.

Carbon fibre reinforced polymers (CFRP) have been used significantly more in recent years due to their increased specific strength over aluminium structures. One major area in which their use has grown is the aerospace industry where many now use CFRP in their construction. One major problem with CFRP’s is their low resistance to impacts. Structural health monitoring (SHM) aims to continually monitor a structure throughout its entire life and can allow aircraft owners to identify impact damage as it occurs. This means that it can be repaired prior to growth, saving weight with the repair and the time that aircraft is grounded. Two areas of SHM being researched are Acoustic Emission (AE) monitoring and AcoustoUltrasonics (AU) both based on an understanding of the propagation of ultrasonic waves. 3D Scanning laser vibrometry was used to monitor the propagation of AU waves with the aim of gaining a better understanding their interaction with delamination in carbon fibre reinforced polymers. Three frequencies were exited with a PZT transducer and the received signal analysed by a cross correlation method. The results from this and the vibrometer scans revealed 100 kHz as the most effective propagating frequency of the three. A high resolution scan was then conducted at this frequency where it could be seen that only the out of plane component of the wave interacted with the damage, in particular the A0 mode. A 3D Fast Fourier Transform was then plotted, which identified the most effective frequency as 160 kHz.

With the increasing complexity of aircraft structures and materials there is an essential need to continually monitor the structure for damage. This also drives the requirement for optimizing the location of sensors for damage detection to ensure full damage detection coverage of the structure whilst minimizing the number of sensors required, hence reducing costs, weight and data processing. An experiment was carried out to investigate the optimal sensor locations of an active sensor network for detecting adhesive disbonds of a stiffened panel. A piezoelectric transducer was coupled to two different stiffened aluminium panels; one healthy and one with a 25.4mm long disbond. The transducer was positioned at five individual locations to assess the effectiveness of damage detection at different transmission locations. One excitation frequency of 100kHz was used for this study. The panels were scanned with a 3D scanning laser vibrometer which represented a network of ‘ideal’ receiving transducers. The responses measured on the disbonded panel were cross- correlated with those measured on the healthy panel at a large number of potential sensor locations. This generated a cost surface which a genetic algorithm could interrogate in order to find the optimal sensor locations for a given size of sensor network. Probabilistic techniques were used to consider multiple disbond location scenarios, in order to optimise the sensor network for maximum probability of detection across a range of disbond locations.

Carbon fibre reinforced polymers (CFRP) have been used significantly more in recent years due to their increased specific strength over aluminium structures. One major area in which their use has grown is the aerospace industry where many now use CFRP in their construction. One major problem with CFRP's is their low resistance to impacts. Structural health monitoring (SHM) aims to continually monitor a structure throughout its entire life and can allow aircraft owners to identify impact damage as it occurs. This means that it can be repaired prior to growth, saving weight with the repair and the time that aircraft is grounded. Two areas of SHM being researched are Acoustic Emission (AE) monitoring and Acousto-Ultrasonics (AU) both based on an understanding of the propagation of ultrasonic waves. 3D Scanning laser vibrometry was used to monitor the propagation of AU waves with the aim of gaining a better understanding their interaction with delamination in carbon fibre reinforced polymers. Three frequencies were exited with a PZT transducer and the received signal analysed by a cross correlation method. The results from this and the vibrometer scans revealed 100 kHz as the most effective propagating frequency of the three. A high resolution scan was then conducted at this frequency where it could be seen that only the out of plane component of the wave interacted with the damage, in particular the A(0) mode. A 3D Fast Fourier Transform was then plotted, which identified the most effective frequency as 160 kHz.