Quantitative Assessment of Drivetrain Alignment Deviations and Their Impact on Wind Turbine Reliability
Keywords:
drivetrain alignment, gearbox reliability, commissioning quality, survival analysis, wind energyAbstract
Drivetrain alignment accuracy is widely recognized as a critical factor in wind turbine reliability, yet quantitative field evidence remains limited. This study investigates the relationship between measured alignment deviations and subsequent failure behavior using field data from 104 utility-scale wind turbines. Laser alignment records collected during commissioning were correlated with gearbox bearing temperatures, vibration spectra, and failure logs over a 30-month period. Regression and survival analyses indicate that axial misalignment exceeding 0.12–0.15 mm increases drivetrain-related failure rates by approximately 17–21%. These findings provide quantitative support for stricter alignment control during installation and commissioning activities
References
1. Liu, S., Liu, X., & Feng, H. (2025, November). Research on AI-Driven Visual Design and Immersive Interactive Experiences Based on Multimodal Cognition and User. In Proceedings of the 2025 International Conference on Digital Society and Intelligent Computing (pp. 734-740).
2. Bechhoefer, E. (2022). Online automatic diagnosis of wind turbine bearings progressive degradations under real experimental conditions based on unsupervised machine learning. Applied Acoustics.
3. Zhang, Y., Gu, W., & Wang, J. (2025). Research on First Article Inspection (FAI)-Driven Quality Assurance Methods for Wind Turbine Installation and Operation & Maintenance and Their Effect on Reliability Improvement. Available at SSRN 6094206.
4. Pandit, R., Astolfi, D., Hong, J., Infield, D., & Santos, M. (2023). SCADA data for wind turbine data-driven condition/performance monitoring: A review on state-of-art, challenges and future trends. Wind Engineering, 47(2), 422-441.
5. Liu, S., Feng, H., & Liu, X. (2025). A Study on the Mechanism of Generative Design Tools' Impact on Visual Language Reconstruction: An Interactive Analysis of Semantic Mapping and User Cognition. Authorea Preprints.
6. Ohdar, S. K., & Behera, S. K. (2025). A comprehensive review on advancements in water-lubricated bearings: Surface texture, tribological performance, and thermal effects. Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology, 239(8), 955-984.
7. Gao, G., Ma, X., Lu, C., & Gao, R. (2026). Reliability Analysis and Application Research of SMS Communication Systems in Medical Notification Scenarios.
8. Bangash, M. F., Arif, A., Hanif, M., Khalil, A., & Imran, A. (2025). AI-Based Multi-Signals Fault Diagnosis of Induction Motor. IEEE Access, 13, 213377-213395.
9. Xu, D., Gui, H., & Chen, H. (2026). Research on Layered Control and Fault Recovery Mechanisms for Fast Charging Safety Diagnosis of High Voltage Battery Systems Under Charging Network Interoperability Conditions.
10. Khatun, Z. (2025). Hybrid Digital Twin and Monte Carlo Simulation For Reliability Of Electrified Manufacturing Lines With High Power Electronics. International Journal of Scientific Interdisciplinary Research, 6(2), 143-194.
11. Zhang, Y., & Wang, J. (2026). Design and Implementation of a Computer-Aided Full Lifecycle Quality Management System for Wind Farms in Upgrades, Renovations, and Subcontractor Supervision.
12. Karkadakattil, A. (2026). Field-Based Comparative Performance Analysis of Passive Cooling Strategies for Photovoltaic Modules in Tropical Climates. Journal of Engineering Research and Reports, 28(2), 142-163.
13. Jiao, Y., Wang, A., Zhao, B., & Shi, T. (2026). The Impact of Visual Language Strategies in Public Art Creation on Community Spatial Perception and Public Behavior.
14. Horridge, R., Stewart, E., Entezami, M., & Yeo, G. (2026). Autonomous alignment of continuous railway track geometry recordings from in-service vehicles. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 240(1), 57-67.
15. Yu, X. (2026). Enhancing Search Efficiency through LLM-Based User Memory Systems for Query Matching and Intent Modeling.
16. Horridge, R., Stewart, E., Entezami, M., & Yeo, G. (2026). Autonomous alignment of continuous railway track geometry recordings from in-service vehicles. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 240(1), 57-67.
17. Yu, X. (2026). AI-Driven Personalization across Domains for Local Categorical Query Understanding and Context-Aware Retrieval.
18. Diwani, M., Mamun, A., & Hassan, S. (2025). A Regression Discontinuity Analysis of the 2022 Russia–Ukraine Conflict and Its Implications for SDG 7 in Europe (No. 24). M&S Research Hub institute.
19. Wang, Y., Feng, Y., Fang, Y., Zhang, S., Jing, T., Li, J., ... & Xu, R. (2025). HERO: Hierarchical Traversable 3D Scene Graphs for Embodied Navigation Among Movable Obstacles. arXiv preprint arXiv:2512.15047.
20. Shadi, M. R., Mirshekali, H., Tahavori, M., & Shaker, H. R. (2026). Survival Models for Predictive Maintenance and Remaining Useful Life in Sensor-Enabled Smart Energy Networks: A Review. Sensors (Basel, Switzerland), 26(6), 1915.
21. Su, X., Du, T., & Wang, X. (2025). Real-Time Motion Planning and High-Precision Control Method for Six-Axis Industrial Robotic Arms Based on Multi-Source Error Compensation.
22. Nejad, A. R., Keller, J., Guo, Y., Sheng, S., Polinder, H., Watson, S., ... & Helsen, J. (2022). Wind turbine drivetrains: state-of-the-art technologies and future development trends. Wind Energy Science, 7(1), 387-411.
23. Gao, X. (2025). Research on Knowledge Stickiness and Bargaining Power Evolution in Distributed Innovation of Electric Power Enterprises Based on the LSSVM-Copula-Bayesian Framework. Available at SSRN 5966594.
24. Khatun, Z. (2025). AI-Driven Predictive Maintenance For Motor Drives In Smart Manufacturing A Scada-To-Edge Deployment Study. American Journal of Interdisciplinary Studies, 6(1), 394-444.
25. Gao, X., Chen, J., Huang, M., & Fang, S. (2025). Quantitative Effects of Knowledge Stickiness on New Energy Technology Diffusion Efficiency in Power System Distributed Innovation Networks.
26. Milani, S., Leoni, J., Cacciola, S., Croce, A., & Tanelli, M. (2025). A machine-learning-based approach for active monitoring of blade pitch misalignment in wind turbines. Wind Energy Science, 10(3), 497-510.
27. Gao, X., Chen, J., & Huang, M. (2026, January). Research on Risk Dependency Structures and Resource Allocation Optimization in New Energy Technology Collaboration within Enterprise Distributed Innovation. In 2026 International Conference on Electrical Engineering, Intelligent Control and Artificial Intelligence (EEICAI) (pp. 43-47). IEEE.
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Copyright (c) 2026 Julien Moreau, Claire Dubois, Antoine Lefèvre
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