摘要
利用风电机组实时监测数据来研究主轴承温度与其潜在故障之间的关系,提出一种基于温度预测模型的风电机组主轴承在线故障预测方法。首先建立正常运行状况下主轴承温度的线性回归分析预测模型,提出可表示系统实际运行状态和预测状态之间偏差的判别函数,通过比较判别函数值与设定门槛值来监控主轴承的运行状态。理论分析和仿真结果表明,该方法所用模型鲁棒性好,提取的故障特征明显,可有效地预测在线风电机组主轴承潜在的故障。
This paper presents an online failure forecast method for main bearing of wind turbine based on temperature pre- diction model by using real time monitoring data of wind turbine for studying relations between temperature of main bearing and potential failure. It firstly establishes a linear regression forecast model of temperature of main bearing under the normal operational condition and proposes discrimination function which can indicate deviation between actual operational state and predictive state of the system. By comparing discrimination function and setting threshold value it can supervise operational state of the main bearing. Theory analysis and simulating result indicate that robustness of the model is good and characteris- tic of the failure is obvious which may effectively predict potential failure of the main bearing of online wind turbine.
出处
《广东电力》
2012年第11期6-9,50,共5页
Guangdong Electric Power
基金
国家自然科学基金资助项目(51077053)
关键词
风电机组
主轴承
回归分析
温度
故障预测
wind turbine~ main bearing
regression analysis
temperature
failure forecast
