摘要
本文面向风电机组变桨轴承运行中常见的卡滞类故障问题,构建以振动信号为核心的故障识别方法,设计特征提取体系并提取RMS、峭度、频率能量等关键参数,建立状态分类模型并完成实验样本构建与识别精度验证。结果显示所提方法在状态区分与识别效率上具备良好性能,能够为风电装备运行状态监测与智能诊断策略提供有效支撑。
This article focuses on the common seizure faults in the operation of pitch bearings in wind turbines.A fault identification method centered on vibration signals is proposed.A feature extraction system is designed to extract key parameters such as RMS(Root Mean Square),kurtosis,and frequency energy.A state classification model is established,and experimental samples are constructed to verify the recognition accuracy.The results show that the proposed method performs well in state differentiation and recognition efficiency,providing effective support for the condition monitoring and intelligent diagnosis strategies of wind power equipment.
作者
韦慧敏
石磊
Wei Huimin;Shi Lei(Guangxi Nanning Huadian Xinneng Wind Power Co.,Ltd.,Nanning,Guangxi,China,530000)
出处
《仪器仪表用户》
2025年第8期67-69,共3页
Instrumentation
关键词
风电机组
变桨轴承
振动诊断
故障识别
wind turbines
pitch bearings
vibration diagnosis
fault identification
