Abstract 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.
Niu et al. (Fri,) studied this question.
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