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As the world increasingly gravitates towards green, environmentally friendly and low-carbon lifestyles, wind power has become one of the most technologically established renewable energy sources. However, with the continuous increase in their output power and height, wind turbine towers are subjected to higher-intensity alternating wind loads. This makes critical components more prone to fatigue failure, potentially leading to major accidents such as tower buckling or turbine collapse. High-strength bolts play a vital role in supporting towers but are susceptible to fatigue crack initiation under long-term cyclic loading, necessitating regular inspection. Types of wind turbine bolts mainly include high-strength bolts, stainless steel bolts, anchor bolts, titanium alloy bolts, and adjustable bolts. These bolts are distributed across different parts of the turbine and perform distinct functions. Among them, high-strength bolts in the tower are particularly critical for structural support, demanding prioritized periodic inspection. Compared to destructive offline inspection methods requiring bolt disassembly, non-destructive testing (NDT) has emerged as a trend in defect detection technologies. Therefore, this review comprehensively examines various types of NDT techniques for wind turbine towers’ high-strength bolts, including disassembly inspection techniques (magnetic particle inspection, penetration inspection, intelligent torque inspection, etc.) and non-disassembly inspection techniques (ultrasonic inspection, radiographic inspection, infrared thermographic inspection, etc.). For each technique, we analyze the fundamental principles, technical characteristics, and limitations, while emphasizing the interconnections between the methodologies. Finally, we discuss potential future research directions for bolt defect NDT technologies.

High-strength bolts are crucial load-bearing components of wind turbine towers. They are highly susceptible to fatigue cracks over long-term service and require timely detection. However, due to the structural complexity and hidden nature of the cracks in wind turbine tower bolts, the small size of the cracks, and their variable propagation directions, detection signals carrying crack information are often drowned out by dense thread signals. Existing non-destructive testing methods are unable to quickly and accurately characterize small cracks at the thread roots. Therefore, we propose an ultrasonic phased array element arrangement method based on the Fermat spiral array. This method can greatly increase the fill rate of the phased array with small element spacing while reducing the effects of grating and sidelobes, thereby achieving high-energy excitation and accurate imaging with the ultrasonic phased array. This has significant theoretical and engineering application value for ensuring the safe and reliable service of key wind turbine components and for promoting the technological development of the wind power industry.

With the variance of preload and vibration in working conditions, wind turbine bolt loosening is difficult to predict accurately. To address the problem, wind turbine bolts are employed as the study object, and the loosening mechanism of bolts as well as the prediction of preload variation are investigated by means of finite element analysis. The result shows that, under the action of transverse vibration load, the magnitude of vibration load is the main factor affecting the loosening, and the larger the load magnitude, the more likely the loosening occurs. Besides, a bolt loosening prediction model based on Gaussian process regression is developed to obtain confidence intervals for the variation of the preload in a probabilistic sense. This study provides a theoretical basis for solving the problem of bolt loosening and preload relaxation in wind power under vibration conditions, and improves the safety and reliability of wind turbine operation.

With the rapid development of the wind energy industry, there is an increasing concern about operation safety and reliability of high-strength wind turbine bolts. The aim of this paper is to monitor the strain change around the cracks in wind turbine bolts by means of fiber Bragg grating (FBG) sensors for crack detection. Firstly, the strain distributions of wind turbine bolts’ cracks with different locations and angles in the service condition are simulated using finite element analysis (FEA). Then, three-point grating string FBG sensors were pasted on the surface of wind turbine bolts with fatigue cracks to monitor the strain changes around the cracks in real time. By analysing the monitored strain data elaborately, the location of the crack on the bolt surface was successfully detected by identifying the location of the maximum strain detected by FBG sensors. In addition, the strain distributions in the vicinity area of the crack at different angles (0°, 45° and 90°) were also monitored and analysed in depth. The different types of crack angles could be distinguished based on of different strain distribution of the vicinity of the crack tip at different angles. The experimental results show that the FBG sensing technology has a high degree of sensitivity and accuracy in crack detection of high-strength wind turbine bolts.

Bolt looseness may occur on wind turbine (WT) blades exposed to operational and environmental variability conditions, which sometimes can cause catastrophic consequences. Therefore, it is necessary to monitor the loosening state of WT blade root bolts. In order to solve this problem, this paper proposes a method to monitor the looseness of blade root bolts using the sensors installed on the WT blade. An experimental platform was first built by installing acceleration and strain sensors for monitoring bolt looseness. Through the physical experiment of blade root bolts' looseness, the response data of blade sensors is then obtained under different bolt looseness numbers and degrees. Afterwards, the sensor signal of the blade root bolts is analyzed in time domain, frequency domain, and time‐frequency domain, and the sensitivity features of various signals are extracted. So the eigenvalue category as the input of the state discrimination model was determined. The LightGBM (light gradient boosting machine) classification algorithm was applied to identify different bolt looseness states for the multi‐domain features. The impact of different combinations of sensor categories and quantities as the data source on the identification results is discussed, and a reference for the selection of sensors is provided. The proposed method can discriminate four bolt states at an accuracy of around 99.8% using 5‐fold cross‐validation.

The loosening and fracture of the blade root bolt, a crucial link between the blade and hub, significantly affect the wind turbine's safe operation. To address this issue, the load redistribution after loosening and fracture of the blade root bolts is considered first. The theoretical model of axial force calculation of the blade root bolts is then deduced and verified through tests. Subsequently, a finite element model of the blade root bolt connection structure is established, and its effectiveness is analyzed. Finally, based on the finite element model, the effects of the preload force, the number of loosened or fractured, and the area of loosened or fractured on the loading characteristics of blade root bolts are investigated. Results show that when the preload force of the loosened blade root bolts is not zero, its axial stress variation law is the same as its preload force variation law. When the preload force is zero, the axial stress of the blade root bolts with zero preload force increases, whereas the axial stress of the non-loosened blade root bolts decreases. Exceeding the material's ultimate strength causes the blade root bolt to fracture. The axial stress in the loosened blade root bolts around the center of the fractured decreases, and the magnitude of the axial stresses of the surrounding non-loosened blade root bolts increases and then decreases along the fractured center to both sides. The findings can offer theoretical guidance for predicting the fatigue life of blade root bolts and their online monitoring.

With the development of wind power industry, the adoption of the wind turbine has achieved continuous growth over the years. As the key component to connect the adjacent segments of the towers, the flange bolts play a vital role in the safe operation of the wind turbine towers. The detection of loose bolts has drawn extensive attention. The primary objectives of this research are to present a method for damage detection of flange bolts in the wind turbine towers. In this paper, the formulation of the damage index based on the dynamic strain responses is first presented based on the mechanical characteristics of the wind turbine towers. Then, a parametric analysis is conducted based on the numerical simulation to investigate the influence of the loose bolt location on the damage index. Some important results are extracted in the parametric analysis, which is further validated by the experimental investigation into a large-scale steel tube specimen whose flange has 12 bolts. Finally, a methodology for damage detection of the flange bolts is proposed, which can identify the regional location of loose bolts.

The most widely used connection form of the wind turbine foundation and the tower is the foundation ring method. With the popularization and application of high wheel hub and large-capacity wind turbines, the anchor cage ring has gradually become the connection method of large-capacity wind turbines. Therefore, the force mechanism of the connection between the foundation ring and the anchor cage ring has become an important issue in the basic design of the wind turbine. In this paper, numerical simulation method is used to analyse the characteristics of the two connection methods and force characteristics of the foundation ring and anchor bolts. The force transmission mechanism and force characteristics of the two connection methods are summarized and the stress distribution of the foundation ring, the anchor cage ring and the nearby concrete are obtained. Finally, the basic strengthening strategies under the two connection methods are clarified.

Critical components in support structures for wind turbines, flange joints, are fundamental to ensure the structural integrity of mechanical assemblies under varying operational conditions. This paper investigates the structural performance of L-type flange joints, focusing on the influence of bolt grades and bolt pretension through a finite element analysis (FEA) study of its key performance indicators, including stress distribution, deformation, and force–displacement behaviors. This paper studies two high-strength bolt grades, Grade 10.9 and Grade 12.9, and two main steps—first, bolt pretension and, second, external loading (tower shell tensile load)—to investigate the influence on joint reliability and safety margins. The novelty of this study lies in its specific focus on static axial loading conditions, unlike the existing literature that emphasizes fatigue or dynamic loads. Results show that the specimen carrying a higher bolt grade (12.9) has 18% more ultimate load carrying capacity than the specimen with a lower bolt grade (10.9). Increased pretension increases the stability of the joint and reduces the micro-movements between A and B (on model specimen), but could result in material fatigue if over-pretensioned. Comparative analysis of the different bolt grades has provided practical guidance on material selection and bolt pretension in L-type flange joints for wind turbine support structures. The findings of this work offer insights into the proper design of robust flange connections for high-demand applications by highlighting a balance among material properties, bolt pretension, and operational conditions, while also proposing optimized pretension and material recommendations validated against classical analytical models.

The failure of wind turbine blade bolts is a critical issue threatening the safe operation of wind turbines. This study investigates the bolt fracture cases of the #7 and #8 wind turbines in a wind farm. Through on-site inspections, fracture analysis, and numerical simulations, the fracture mechanism is revealed, and systematic prevention and control strategies are proposed. Failure mechanism analysis indicates that the primary cause of bolt fracture is insufficient preloading, leading to a decrease in fatigue strength. Crack initiation occurred in the stress concentration area of the threads (the 3rd to 5th threads), and the fracture surface displayed typical fatigue striations and brittle fracture features. Indirect causes include inadequate design specification compatibility (the M30 bolt diameter is significantly smaller than the M36 standard used in similar models, with a tensile strength reduction of approximately 30%) and installation process defects (such as uneven application of lubricants and hydraulic tensioning tool errors). The optimization strategies proposed include: ① adopting ultrasonic preloading direct measurement technology (accuracy ±3%) combined with electromagnetic ultrasonic axial force detection for early bolt damage identification; ② upgrading the bolt specification to M36 and optimizing the thread transition fillet (R≥1mm). Finite element analysis shows that the allowable stress increases from 854.5 MPa to 940 MPa after the improvement; ③ standardizing the installation process by implementing the torque-angle dual control method and full-thread lubrication to reduce preloading variability (tightening coefficient reduced from 1.4 to 1.3); ④ establishing a bolt health archive and an online monitoring system, with real-time early warning using a multi-channel ultrasonic device. After implementing these measures in a project, the fault response efficiency improved by 70%.The study validates that these measures can reduce the fracture risk by 50% and provide empirical cases for preventive maintenance of wind turbines. In the future, integrating digital twins and machine learning technologies will enable bolt life prediction and proactive maintenance, advancing the intelligent transformation of wind turbine operations and maintenance.