基于机器学习的振动信号分析,用于在低功率风力轮机中检测裂
Angel H Rangel-Rodriguez1, David Granados-Lieberman2, Juan P Amezquita-Sanchez1
1ENAP-Research Group, CA-Sistemas Dinámicos y Control, Facultad de Ingeniería, Universidad Autónoma de Querétaro (UAQ), Campus San Juan del Río, Río Moctezuma 249, Col. San Cayetano, San Juan del Río 76807, Mexico.
Entropy (Basel, Switzerland)
|August 26, 2023
概括
风力轮机叶片裂的早期检测对于高效的维护至关重要. 这项研究使用机器学习准确地从振动信号中分类裂纹严重程度,达到99.5%以上的准确性.
科学领域:
- 工程 工程师 工程师 工程师
- 可再生能源系统可再生能源系统
- 材料科学 材料科学 材料科学
背景情况:
- 风力轮机 (WTs) 对可再生能源至关重要,但易受环境破坏的影响.
- 叶片损坏,特别是裂,降低了效率,增加了成本,需要早期检测.
- 状态监测对于维护WT的运行完整性和寿命至关重要.
研究的目的:
- 开发和验证一种基于机器学习的方法,用于检测和评估风力轮机叶片裂的严重程度.
- 在各种风力条件下分析振动信号,以识别明显的裂纹特征.
- 为风能基础设施的积极维护计划提供可靠的工具.
主要方法:
- 分析了WT刀片带有健康,轻,中等和严重裂的振动信号.
- 特征提取涉及统计和和指数,随后使用差异分析 (ANOVA) 进行特征选择.
- 使用k-最近邻近算法进行分类,并与神经网络,决策树和支持向量机器进行比较.
主要成果:
- 拟议的机器学习方法实现了超过99.5%的分类准确性,用于检测和评估裂严重程度.
- 该方法有效地区分了各种裂纹水平 (健康,轻,中等,严重) 基于振动信号分析.
- 在特征选择方面,ANOVA被证明是有效的,提高了k-近邻模型的分类性能.
结论:
- 对振动信号的机器学习分析提供了一个非常准确的方法来检测和分类风力轮机叶片裂.
- 这种技术可实现精确的状态监测,这对于优化维护策略和降低风电场运营成本至关重要.
- 该研究证明了人工智能驱动的诊断在确保风能系统的可靠性和效率方面的潜力.
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