Monitoring fatigue damage in mechanical connections is essential for maintaining the safety and structural integrity of offshore wind turbines (OWTs), particularly during the early stage of crack initiation. Recently, the C1 wedge connection (C1-WC) has emerged as a promising innovation for use in OWTs. Acoustic emission (AE) monitoring is a widely used real-time technique for detecting fatigue cracks. The space limitations of the lower segment holes in the C1-WC presents challenges for detecting surface cracks with conventional AE sensors. Thin Piezoelectric Wafer Active Sensors (PWAS), while small and lightweight, face limitations due to their poor signal-to-noise ratio. In this study, we propose a baseline-based approach to enhance the effectiveness of PWAS for accurate AE monitoring in confined spaces. A benchmark model correlating the damage state of specimens is created by breaking pencil leads. Multivariate feature vectors are extracted and then mapped to the Mahalanobis distance for damage identification. The proposed method is validated through testing on compact specimens and C1-WC specimens. To enhance the AE detection results, supplementary monitoring techniques, including digital image correlation, crack propagation gauges, and distributed optical fiber sensors, are employed. The experimental setup, signal acquisition, and detection efficiency of these techniques are briefly outlined. This study demonstrates that the proposed approach is highly effective in detecting early damage in C1-WC specimens using AE monitoring with PWAS.

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

The acoustic emission (AE) technique is commonly utilized for identifying source mechanisms and material damage. In applications requiring numerous sensors and limited detection areas, achieving significant cost savings, weight reduction, and miniaturization of AE sensors is crucial. This prevents excessive weight burdens on structures while minimizing interference with structural integrity. Thin Piezoelectric Wafer Active Sensors (PWAS), compared to conventional commercially available sensors, offer a miniature, lightweight, and affordable alternative. The low signal-to-noise ratio (SNR) of PWAS sensors and their limited effectiveness in monitoring thick structures result in the decreased reliability of a single classical PWAS sensor for damage detection. This research aims to enhance the functionality of PWAS in AE applications by employing multiple thin PWAS and performing a data-level fusion of their outputs. To achieve this, as a first step, the selection of the optimal PWAS is performed and a configuration is designed for multiple sensors. Pencil break lead (PBL) tests were performed to investigate the compatibility between selected PWAS and traditional WSα and R15α sensors. The responses of all sensors from different AE sources were compared in both the time and frequency domains. After that, convolutional neural networks (CNNs) combined with principal component analysis (PCA) are proposed for signal processing and data fusion. The signals generated by the PBL tests were used for network training and evaluation. This approach, developed by the authors, fuses the signals from multiple PWAS and reconstructs the signals obtained from conventional bulky AE sensors for damage detection. Three CNNs with different architectures were built and tested to optimize the network. It is found that the proposed methodology can effectively reconstruct and identify the PBL signals with high precision. The results demonstrate the feasibility of using a deep-learning-based method for AE monitoring using PWAS for real engineering structures.

Hole patterns are common in engineering design for connections and/or assembly purposes. Geometrical discontinuities can cause stress concentration in localized areas, making them more prone to fatigue crack initiation and influencing the fatigue life of the overall unit. In the past, much effort has been exerted on fatigue modelling of holed plates from both experimental and theoretical perspectives. However, most studied objects were aluminium or titanium thin plates for aviation purposes. In this work, the fatigue performance of a downscaled holed thick steel plate, extracted from a novel C1 Wedge Connection for wind turbine tower assembling, was tested and categorized according to commonly used industry codes. In particular, the influence of the surface size effect was experimentally observed and computationally discussed. Finally, a probabilistic fatigue model was proposed, which gives a favourable prediction on the fatigue behaviour of the surface polished holed thick steel plate with the help of the Smith–Watson–Topper (SWT) model.

The overall competitiveness of offshore wind turbine towers is significantly influenced by the selection of the connection. The following three types of connections: a conventional bolted ring flange (RF) connection, ring flange connection with defined contact surfaces (RFD), and C1 wedge connection (C1-WC) are considered. A quantitative comparison is made to enhance performance in a specific condition and enable further optimization of these connections in engineering practices. The study compares the tensile behaviour and fatigue performance of these connections by validated finite element (FE) simulation and analysis. The proposed FE modelling is based on a realistic geometry including all contacts present in the connections, steel full-range stress–strain relationship and ductile damage model. The efficiency and accuracy of the FE models are validated through the comparison with the performed tests. Then, a series of parametric FE analyses are carried out to examine the impact of the applied boundary conditions, bolt pretension level, and steel grade on the behaviour of connections. Load-displacement curves, bolt evolution curves, and stress responses are analysed to compare their tensile behaviour. The effectiveness of conventional segment specimen testing is evaluated thoroughly. For the fatigue performance of connections, the results indicate that the segment specimen testing substantially underestimates the fatigue performance of C1-WCs. This discrepancy is essential to be considered in the tower design. It is also noted that C1-WCs are rather insensitive to the variation of pretension force level, which show superiority to avoiding the difficulties associated with typically bolted joints. This research provides in-depth knowledge for the practical application of such connections and further optimization.

The performance of the Acoustic Emission (AE) technique is significantly dependent on the sensors attached to the structural surface. Although conventional commercially AE sensors, like R15a and WSa sensors, have been extensively employed in monitoring many different structures, they are unavailable in restricted-assess areas. In contrast, thin PZT sensors are small, inexpensive and lightweight. These thin PZT sensors have a great potential for passive sensing to detect AE signals. However, their utility in AE monitoring is limited due to their low signal-to-noise ratio and information incompleteness because of their simple construction. This work discusses the issues and possible solutions with regards to the specific selection and application of thin PZT sensors for passive sensing. The compatibility of different thin PZT sensors and conventional bulky sensors is investigated using pencil break lead (PBL) tests. The comparison between the recorded signals is carried out in both the time domain and frequency domain for these sensors. To improve the reliability and performance of the thin PZT sensors, a methodology employing multiple thin PZT sensors of different sizes is proposed based on machine learning techniques and sensor data fusion.

This paper shows a part of the analysis of the development of the second generation of the C1 wedge connections for use in offshore wind turbine supporting towers. The novelty of this connection is that bolt failure is avoided under static and fatigue loads. This study aims to investigate the tensile behaviour of the connection by combining the findings of experiments and finite element (FE) analysis. Two specimens subjected to uniaxial and cyclic tensile loading tested until failure are used for illustration. Advanced quasi-static FE analysis results, considering the most detailed geometry and using an explicit dynamic solver, are compared to the experimental results. The FE analysis results agree well with the experimental results. Based on the FE model, a parametric study is carried out to analyse the influence of the bolt grade, friction coefficient between contact surfaces, and preloading force level on mechanical behaviour. Failure modes, bolt force development, and the evolution of gap opening between contacted segments are analysed. Results demonstrate that the tensile fracture of the C1 wedge connection mainly appears in the lower segment. All the investigated parameters have a negligible effect on the connection's ultimate resistance and failure mode. However, the friction coefficient between contact surfaces and bolt preload level significantly affects the connection's local deformation capacity and the response of the bolt stress range. The FE simulation provides practical guidance for designing this connection without bolt failure.

Acoustic emission (AE) is often used for structural health monitoring (SHM) in the wide field of engineering structures and one of its most beneficial attributes is the ability to localize the damage/crack based on the AE events. The vast majority of ongoing work on AE monitoring focues on geometrically simple structures or a confined area, but the AE source location strategies are rather complicated for real engineering structures. In this paper, an effective method for source localization in realistic structures is presented based on the application of artificial neural networks (ANN), using finite element (FE) simulation results of Lamb waves as the modelling basis. Pencil lead break experiments and related FE simulations on a steel-concrete composite girder are conducted to evaluate the performance of the method. The identification of different wave modes is carried by comparing alternative onset time detection methods. Numerical results are found to be matching closely with the experimental results. To get a reliable ANN model, the validated FE model is used to create a comprehensive database with five different sensor arrangements. It is found that the proposed method is superior to the classical Time of Arrival (TOA) method with the same input data. The results indicate that using trained neural networks based on numerical data is a viable option for AE source location in the case of the I-shaped girder, increasing the likelihood of design and optimization of the AE technique in monitoring realistic structures.