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Vibration monitoring and signal analysis of wind turbine drivetrains based on fiber bragg grating sensing
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DOI：10.1016/j.optlastec.2026.116117.png)
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Abstract
En 中文
The drivetrain of wind turbine is essential to wind energy conversion process, and its operational condition directly impacts the safety of wind turbine. Vibration monitoring is a crucial method for evaluating the state of drivetrains. Currently, fiber optic grating sensor technology is primarily used for vibration monitoring of individual components in wind turbines, with most studies still in the experimental verification stage. Therefore, this paper presents a study on the synchronous monitoring of multiple wind turbine drivetrains using fiber Bragg grating (FBG) vibration sensors. The research focuses on four operational wind turbines, and calibrated FBG sensors are deployed at eight key locations within the drivetrain. Vibration data is collected through the FBG sensing system and analyzed using time-domain statistical methods. The results indicate that the acceleration coefficient of the sensors fluctuates between 0.015 and 0.065, with values are relatively concentrated and exhibiting good consistency. The measured vibration signals exhibited a wide dynamic range, with the maximum positive acceleration exceeding 100 m/s2 and the maximum negative acceleration exceeding 80 m/s2. This comprehensive coverage effectively reflected the actual vibration conditions of the drivetrain during operation. The vibration characteristics of the drivetrain components exhibited reasonable individual differences across all turbines. The FBG sensors demonstrate significant advantages, including the ability to provide real-time monitoring across the entire drivetrain system. This research improves the use of FBG sensors in wind turbine drivetrain monitoring and provides a solid data foundation for future applications, such as vibration monitoring location selection and drivetrain condition assessment.
Keywords:
Wind turbine drivetrain
Fiber Bragg Grating (FBG) vibration sensor
Multi-turbine synchronized monitoring
Time-domain statistical feature analysis
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