In the past two decades, offshore wind has emerged as a new source of renewable energy. This highlights the requirement for the utilisation of larger and more efficient offshore wind turbines (OWTs). The connections used in support structures of OWTs are critical to ensure the excellent structural performance of OWFs. An alternative option is the C1 wedge connection (C1-WC) to join virtually all the wind turbine generator (WTG) towers to their foundations. This connection shows promising potential in reducing construction, installation, and maintenance costs by eliminating the ring flange and using smaller diameter bolts. Till now, C1-WC has undergone three generations of development. A more comprehensive research program is required to explore its implementation in a wind farm. The load transfer mechanism and critical component of C1 wedge connections are different to the conventional bolted ring flange (RF) connections. It is important to understand the mechanical behaviour of this connection. Meanwhile, support structures are exposed to the harsh environment during the service life of the OWTs. Material degradation and local cracks in the connection are inevitable to affect the serviceability of OWTs. A need for a reliable and rigorous structural health monitoring (SHM) system for the connection is evident. As one of the non-destructive techniques (NDT), Acoustic emission (AE) has been extensively used in early damage detection and real-time assessment of steel structures. Despite its successful applications, challenges still exist in using AE technique for monitoring applications, especially in analysing the recorded data. Therefore, the research aims to assist in understanding mechanical behaviour and evaluating the health status of the innovative connection. An extensive experimental program was conducted to evaluate the static and cyclic behaviour of the C1-WCs. Additionally, a detailed 3D non-linear finite element (FE) model of the C1-WCs has been developed. The incorporation of material non-linearity and ductile damage allows the FE model to model the post-necking and final fracture of the connection. The FE model replicates with a good agreement the experimental tensile static and cyclic tests up to the final damage and reproduces the joint behaviour correctly. Parametric studies investigate the influence of bolt grade, the friction coefficient between contact surfaces, and the preloading force level on mechanical behaviour. Moreover, a quantitative comparison between C1-WC and two types of connections (RF connection and RF connection with defined contacts) is performed to provide practical insights into the selection and application of such connections and further optimization. FE-assisted analyses were performed to examine the effect of applied boundary conditions, bolt pretension level, and steel grade on the behaviour of the connections. In addition to mechanical behaviour analysis. this research is also focused on developing data processing methods to address the challenges of AE monitoring for the C1-WCs. A hybrid model is proposed to identify the deformation stage of metal material. This method combines a self-adaptive denoising technique and an Artificial neural network (ANN). To reduce noise in the AE signals, a decomposition-based denoising method is proposed based on singular spectral analysis (SSA) and variable mode decomposition (VMD), referred to as SSA-VMD. After denoising, an ANN is constructed to identify the deformation stage of steel materials using features extracted from the filtered AE signals as input. Fatigue damage of the C1-WCs could result in catastrophic failure of OWTs. Due to space constraints, it can be challenging to detect surface cracks in the lower segment holes of the C1-WC using commercial sensors. Thin PZT sensors are lightweight and small, making them suitable for use in restricted-access areas. However, their poor signal-to-noise ratio can limit their effectiveness in AE monitoring. A criterion for selecting the optimal thin PZT sensors is proposed and a configuration is designed for multiple sensors. Two signal processing methods are then proposed in terms of this issue. Firstly, a data fusion-based method is proposed to enhance the functionality of thin PZT sensors in AE applications. Convolutional neural networks (CNNs) combined with principal component analysis (PCA) are employed for signal processing and data fusion. Secondly, a baseline-based method is proposed to provide early warning of the fatigue damage of C1-WCs using thin PZT sensors. A benchmark model correlating to the damage state is created by breaking pencil leads. Multi-variate feature vectors are extracted and then mapped to the Mahalanois distance for identification. Based on this research work, an efficient FE method has been developed to further improve the design of C1-WC. By providing an in-depth guideline for evaluating the mechanical performance of connections used in OWTs, this research has the potential to contribute to the development of more robust and reliable wind turbine structures. Moreover, the proposed signal processing methods for identifying the deformation stage and early fatigue damage can be further explored in structures with similar damage mechanisms. This can lead to the development of more accurate and effective methods for monitoring and assessing the health of offshore wind turbines, ultimately contributing to improved safety and reliability in the renewable energy industry. @en

The objective of this master project is to improve the current SHM techniques for global damage identification of beam-like composite structures. The studied damage diagnosis method is based on structural vibrations from which the modal parameters are obtained. The literature study provides an overview of the most common vibration-based damage identification methods. The modal curvature shape- and modal strain energy-based methods are selected due to their higher sensitivity to local damages. These methods are applied on an aero-elastically tailored composite wing. High-spatial-resolution modal shapes are extracted from the structure with a state-of-the-art fibre optic strain sensing technology based on Rayleigh back-scattering. Damages are simulated in the wing employing localized mass attachments. The achievable level of damage identification with this technique is found and characterized. The different vibration-based methods are compared and their potential is discussed. It is concluded that the used sensing technique is an excellent choice for global damage diagnosis in composite structures.

Impact on composite structures shows a different damage behaviour compared to metal structures. Due to the current short operational life time of composite aircraft the risks of impact damages on composite structures are unknown. This paper proposes a new method for quantitative risk analysis of low velocity impact damages on composite aircraft structures by combining a conventional risk analysis with the probability of detection of the damages. Real damage data of metal structures is used to estimate impactors and to predict damages on composite structures by means of an aircraft impact damage model. To create a large set of impact events and to conduct a more accurate analysis impactor data estimated from the real metal damage data is augmented. Three fuselage sections with a significant difference in amount of damages have been selected to compare the different risk results. The outcome of a conventional risk analysis and the outcome of the probability of detection of damages show that the section with the highest amount of damages results into the highest risk. However, the proposed method by combining the probability of detection of the damages with a conventional risk analysis shows different and more revealing results. The fuselage section with nearly 50% less damages compared to the section with the highest damages appears to be the highest risk section but the difference with the other fuselage sections is small. The proposed risk analysis method intends to be a useful tool for aircraft maintenance organisations for a different approach of assessing the risks of impact damages on composite structures.

This paper examines the end-to-end development process for a Convolution Neural Network (CNN) based damage classification tool for ultrasonic inspection of aerospace-grade composite structures. The recent advent of Artificial Intelligence (AI) and Machine Learning (ML) has piqued the interest of the aerospace industry since it has the potential to improve performance and alleviate the burden on personnel. The big question in the industry right now is how and where to introduce this technology while assuring the safety and reliability of its implementation. Guidelines drafted by the European Aviation Safety Agency (EASA) for the development of AI showed that maintenance and training were the most accessible points of entry for this technology as it did not have the same stringent requirements that a flying system would have. This paper proposes a research methodology which allows for the cost-effective development of ultrasonic data for the training and testing of data-driven tools. This was partly achieved by using a novel eFlaw technique which has been implemented for the first time in composite structures. The method allows for significant augmentation and generalisation of datasets, resulting in a model with the ability to detect features potentially smaller than one-quarter of a wavelength. This improved performance paves the way for more sensitive low-frequency ultrasonic inspection in thick composites. To evaluate these models, various evaluation techniques were compared and showed that Receiver operator curves and confusion matrix-derived metrics provided comparable results. Explainable methods found that the GradCam and the inspection of feature maps showed the most interpretable results on the features that were being identified. Using the feature maps it was possible to generate a new type of C-scan, called an F-scan (Feature-scan) which provides an inspector with a view of the C-scan from the perspective of a feature map from the model providing an interpretable view of the model’s classifications. In addition to these positive results, this thesis provides readers with a cost-effective methodology for developing data-driven tools for maintenance applications within the aerospace industry.

Compared to metals, composite materials offer higher stiffness, more resilience to corrosion, have lighter weights, and their mechanical properties can be tailored by their layup configuration. Despite these features, composite materials are susceptible to a diversity of damages, including matrix cracks, delamination, and fibre breakage. If these damages are not detected and mended, they can spread and result in the failure of the whole structure. In particular, when the structure is under fatigue and vibrations during flight, this process can expedite. Moreover, if such damages occur in the internal layers of the composite material, they will be difficult to detect and to characterise. There is thus a huge demand for reliable and accurate structural health monitoring methods to identify these defects. Such methods either try to monitor the structural integrity of the composite during service, or they are used for studying a desired configuration of a composite material during fatigue and tensile tests. This thesis provides structural health monitoring solutions that can potentially be used for both these categories. The structural health monitoring applications developed in this thesis range from accurate strain and displacement measurement, to detection of cracks and the identification of damages in composites. In this thesis, fibre Bragg grating (FBG) sensors were chosen for this purpose. The miniature size and small diameter of these sensors makes them an ideal candidate for embedding them between composite layers, without severely altering the mechanical properties of the host composite material. They can thus provide us with direct information about the current state of the laminated composite, potentially at any depth. This is especially useful for acquiring information about the internal layers of the composite material, as barely visible impact damages and micro-cracks often form beneath the surface of the material without being visible on its exterior. In spite of their interesting physical characteristics, applications of FBG sensors are typically limited to point strain or temperature sensors. Further, it is often assumed that the strain field along the sensor length is uniform. For this reason, there is currently a gap in the field of structural health monitoring in retrieving meaningful information about the non-uniform strain field to which the FBG sensor is subjected in damaged structures. The focus of this thesis is on analysing the response of FBG sensors to highly non-uniform strain fields, which are a characteristic of the existence of damage in composites. To tackle this problem, first a new model for the analysis of FBG responses to nonuniform strain fields will be presented. Using this model, two algorithms are presented to accurately estimate the average of such non-uniform axial strain fields, which conventional strain estimation algorithms fail to deliver. In fact, it is shown that the state-of-the-art strain estimation methods using FBG sensors can lead to errors of up to a few thousand microstrains, and the presented algorithms in this thesis can compensate for such errors. It was also shown that these methods are robust against spectral noise from the interrogation system, which can pave the way for more affordable FBG based strain estimation solutions. Another contribution of this thesis is the demonstration of two new algorithms for thedetection of matrix cracks, and for accurate monitoring of the delamination growth in composites, using conventional FBG sensors. These algorithms are in particular useful for studying the mechanical behaviour of laminated composites in laboratory setups. For instance, the matrix crack detection algorithm is capable of characterising internal transverse cracks along the FBG length during tensile tests. Along the same lines, the delamination growth monitoring algorithm can accurately localise the delamination crack tip along the FBG length in mode-I tensile and fatigue tests. These algorithms can perform in real-time, which makes them ideal for dynamic measurement of crack propagation under fatigue, and their spatial resolution and accuracy is superior to the other state-of-the-art damage detection techniques. Finally, to enhance the precision of the damage detection schemes presented in this thesis, two different methods are proposed to accurately determine the active gauge length of the FBG sensor, and its position along the optical fibre. This information is generally not provided for commercial FBG sensors with such accuracy, which can adversely affect the precision of crack tip localisation algorithms. Following the algorithms provided in this thesis, the sensor position can be marked on the optical fibre with micrometer accuracy. @en

One of the classical solutions to maintain the aircraft structural integrity is to rely on the analysis of non-destructive testing (NDT) inspector with various inspection methods. However, it is relatively expensive in matter of time and costs to train human resources until the certification is reached. Further, in majority of the cases of aircraft scheduled and unscheduled maintenance, most of the detected damages are far below the damage tolerance limit and therefore are considered as a costly false positive because such inspections generally require additional downtime. Structural Health Monitoring (SHM) tries to reduce the wasteful resources in the maintenance, repair, and overhaul (MRO) industry by signaling such false positives during the maintenance process by becoming an integral part of the structure itself. On the other hand, there has been an increase in using the artificial intelligence (AI) methodologies such as computational heuristics and machine learning in many areas of human civilization which includes voice and face recognition, languages translation, and automated driving. There has been a lot of interest on implementing AI to assist SHM in maintaining airworthiness while driving the cost down. Nevertheless, the maintenance of airworthiness (such as but not limited to, EASA Part 145/M and FAA CFR Part 21) is a heavily regulated area and are not easily changed. The current state of the art was captured in the literature review. This includes recent developments of guided wave based SHM and the parameter optimization as well as recent trends and advances in artificial intelligence such as machine and deep learning. The findings from the state of the art were used as the basis to determine the research problem and to propose the solution. The first part of the proposed solution consisted of a short review the damage growth assumption within the damage tolerance framework and the used methodology to generate and capture Lamb wave signal within Finite Element (FE) environment. This methodology is a deterministic solution that can be partially used for solving continuous optimization in deterministic sensor placement problem. It was further expanded to include a semi-stochastic approach to address nonpredictable damage location that includes some metaheuristics search such as genetic algorithm and swarm intelligence. The ultimate first part of solution was a compromise between the deterministic and semi-stochastic actuator-sensor topology. The second part of the proposed solution was the investigation on whether deep learning can be used to treat the Lamb wave signal given the configuration obtained from the first part of the proposed solution. To do so, an assumption based on converging probability measures and generalization bound in deep learning must be taken. Then, the approach is to represent the entity of the captured Lamb wave signal in time-frequency domain either as randomly sampled spectrogram or layers of joined spectrograms. After the training, the hypothesis was validated with A/B Testing. Then, the research was expanded to understand the scalability level of deep learning for SHM for given data size, model parameters, and restriction on physical memory. In this sense, the signal representations were trained sequentially with an example of in hybrid convolutional recurrent network. The investigation was focused on stability behavior of convoluted-recurrent modelling for variable spectrogram length and the experimental validation of the model for classification of the Lamb wave spectrogram signals. @en