Enhancing Wind Turbine Gearbox Reliability through Advanced Condition Monitoring and Predictive Maintenance 机器收录·待核验

mxdsy2qp5zx51g39

Wind turbine gearboxes are critical components in wind energy systems but are highly susceptible to mechanical failures due to high loads, variable operating conditions, and material fatigue. This study presents an integrated approach utilizing condition monitoring systems (CMS), predictive maintenance algorithms, and finite element analysis (FEA) to enhance gearbox reliability. By analyzing stress distribution, vibration, and temperature trends, we establish a robust methodology for early fault detection. Our results show that artificial intelligence (AI)-based predictive maintenance can reduce unplanned gearbox failures by 40% and increase overall efficiency by 20%. This study provides insights into optimizing wind turbine operation through data-driven maintenance strategies.

本卡描述如含来自官方页的原文片段,其版权归原作者,不在本站 CC-BY 4.0 许可范围(见关于与许可)。

落地页

https://data.mendeley.com/datasets/2jzv5d4v7v

许可证

CC-BY-4.0 (置信:verified_official)

发布年份

2025

发布方

Mendeley Data

分发点