基于振动数据集分析的风力轮机叶片故障诊断
Ahmed Ali Farhan Ogaili1,2, Alaa Abdulhady Jaber1, Mohsin Noori Hamzah1
1Mechanical Engineering Department, University of Technology, Baghdad, Iraq.
Data in brief
|July 31, 2023
概括
这项研究介绍了故障风力轮机叶片振动的数据集,以改善状态监测. 这些数据有助于理解故障信号,以提高风能可靠性.
科学领域:
- 可再生能源工程可再生能源工程
- 机械振动 - 机械振动
- 状态监控 状态监控
背景情况:
- 风力轮机对于可持续能源至关重要,但故障会增加成本.
- 传统的设计使风力轮机故障的识别变得困难.
- 可靠的运行需要有效的状态监控.
研究的目的:
- 呈现一组来自故障风力轮机叶片的振动数据.
- 覆盖各种故障 (侵蚀,裂,失衡,扭曲) 和操作条件.
- 为了支持条件监测技术的验证.
主要方法:
- 振动信号的数据集编译.
- 包括特定的故障类型:表面侵蚀,破裂的叶片,质量失衡,扭转叶片故障.
- 在不同的操作条件下记录数据,包括风速.
主要成果:
- 该数据集捕获了不同类型故障的振动信号.
- 它包括与常见激发机制相关的数据.
- 提供了分析振动信号特征的基础.
结论:
- 这一数据集是验证工业风力轮机状况监测的宝贵资源.
- 它增强了与特定刀片故障相关的振动信号的理解.
- 有助于提高风能可靠性和降低风能运营成本.
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