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The strain response of the gearbox rotation, detected by DFOS

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The strain response of the gearbox rotation, detected by DFOS
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The rotation slowed down, changed direction and speeded up.
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CC Attribution 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
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Production Year2023-2024
Production PlaceLawrence Berkeley National Laboratory

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Abstract
Paper title: Wind Turbine Gearbox Operation Monitoring Using High-Resolution Distributed Fiber Optic Sensing; Efficient gearbox monitoring is vital for improving fault detection, enhancing design, and reducing operation and maintenance (O&M) costs, particularly for offshore wind turbines. This paper introduces an innovative approach using high-resolution Distributed Fiber Optic Sensing (DFOS) based on Optical Frequency Domain Reflectometry (OFDR) to measure gearbox strain in real time. By bonding a single optical fiber around the full circumference of a 2.152 m diameter planetary stage in a 3.75 MW wind turbine gearbox, we captured millimeter-scale distributed strain profiles of planetary gears under different input torque levels. Our results show accurate identification of planet gear locations in real time and rotation speed (10.42 revolutions per minute) and a strong linear correlation between applied torque and measured strain across all monitored locations (R^2=0.9997), with data collected every 2.6 mm. Strain variations of approximately 200 microstrain were observed on individual gear teeth during engagement, providing granular insights into mechanical behavior and load distribution. Additionally, DFOS detected temperature variations during operation, highlighting its capability to concurrently monitor thermal and mechanical anomalies. This study represents the first application of continuous DFOS to a full-scale wind turbine gearbox. The approach offers a scalable and practical solution for early fault detection, improved mechanical performance, and more reliable wind turbine operations, addressing critical challenges in the wind energy sector.
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