摘要
为提高风电机组齿轮箱的状态监测精度,降低故障率,采用了基于改进集成KNN回归算法方法。通过结合特征选择优化、集成学习技术,设计并实现了风电机组齿轮箱状态监测系统,该系统包括数据采集、处理分析、故障预测等模块。研究以实际风电场的运维数据为例,进行了系统和测试。结果表明,改进后的KNN回归算法显著提升了故障诊断的准确性,系统能实时准确地监测齿轮箱的运行状态,从而提高风电机组的运行可靠性与维护效率。研究成果不仅优化了风电机组的维护策略,也为类似领域的设备状态监测提供了可行的技术路径。
In order to improve the accuracy of the condition monitoring of wind turbine gearboxes and reduce the prevention of failures,this study adopts the method based on the improved integrated KNN regression algorithm.By combining feature selection optimization and integrated learning technology,a wind turbine gearbox condition monitoring system is designed and implemented,which includes modules of data acquisition,processing and analysis,and fault prediction.The system and tests are carried out by taking the operation and maintenance data of actual wind farms as an example.The results show that the modified KNN regression algorithm significantly improves the accuracy of fault diagnosis,and the system can accurately monitor the operating status of the gearbox in real time,so as to improve the operational reliability and maintenance efficiency of wind turbines.The research results not only optimize the maintenance strategy of wind turbines,but also provide a feasible technical path for equipment condition monitoring in similar fields.
作者
左荣荣
Zuo Rongrong(Taiyuan Heavy Industry Co.,Ltd.,Taiyuan Shanxi 030051,China)
出处
《机械管理开发》
2025年第1期191-193,196,共4页
Mechanical Management and Development
