风力轮机轮机油的新诊断方法,结合逐步多变量回归和集群联合学习框架
Huihui Han1, Ye Zhao1, Hao Jiang2
1East China Electric Power Test and Research Institute, China Datang Corporation Science and Technology Research Institute Co., Ltd, Hefei, 230022, China.
Scientific reports
|July 2, 2025
概括
这项研究引入了风力轮轮轮油诊断的集群联合学习框架 (CFLF),提高了35.73%的准确性. 这种数据驱动的方法提高了风能部门的可靠性.
科学领域:
- 工程 工程师 工程师 工程师
- 数据科学数据科学数据科学
- 可再生能源可再生能源是可再生能源.
背景情况:
- 风力轮机轮机油诊断对于运行可靠性至关重要.
- 现有的数据驱动方法面临着数据异质性的挑战.
研究的目的:
- 通过使用新的集群联合学习框架 (CFLF) 增强风力轮轮轮油故障诊断.
- 提高风能部门的诊断准确性和可靠性.
主要方法:
- 开发了一种步骤多变量回归 (SMR) 模型,集成多尺度和AIC诊断特征.
- 应用了正统的相关性表示来解决数据异质性.
- 建议使用CFLF和SMR组合模型来评估轮油性能.
主要成果:
- 与独立的SMR模型相比,拟议的CFLF-SMR模型表现出优越的性能.
- 在实际风力轮轮轮油数据分析中,预测准确度大幅提高35.73%.
结论:
- CFLF-SMR模型为风力轮轮轮轮油评估提供了一个强大而准确的技术.
- 这种方法提高了诊断能力,有助于风能系统的可靠性.
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