单传感器冲击源定位方法用于无异型玻璃纤维复合式风力轮机叶片
Liping Huang1, Kai Lu1, Liang Zeng2
1School of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
一种新的深度学习方法使用单个传感器准确地定位风力轮机叶片撞击的位置. 这种具有成本效益的方法实现了96.9%的准确性,改善了结构健康监测,与传统的多传感器系统相比降低了成本.
科学领域:
- 工程 工程师 工程师 工程师
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 风力轮机叶片在运行过程中面临各种冲击 (鸟类撞击,闪电,冰).
- 准确的冲击源定位对于有效的结构健康监测 (SHM) 至关重要.
- 传统的本地化方法通常需要复杂的多传感器阵列和异型性补偿.
研究的目的:
- 提出一种新的单一传感器方法,用于定位风力轮机叶片上的冲击源.
- 利用深度学习将影响本地化转化为分类任务.
- 为了降低风力轮机的SHM系统的成本和复杂性.
主要方法:
- 开发了一个深度学习框架,用于影响源本地化.
- 将局部化视为分类问题,避免异性异性补偿.
- 利用刀片材料和几何学的固有异型效应来实现单传感器定位.
主要成果:
- 用单个传感器实现了96.9%的高定位精度.
- 证明了深度学习方法在分类影响位置方面的有效性.
- 与传统的多传感器阵列方案相比,大大降低了成本.
结论:
- 拟议的单一传感器方法为风力轮机叶片撞击探测提供了具有成本效益和准确的解决方案.
- 深度学习有效地解决了在SHM中的影响本地化挑战.
- 这种方法提高了风力轮机结构健康监测的可靠性和经济可行性.
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