风电机组变桨轴承失效原因分析

Analysis of Failure Causes of Wind Turbine Pitch Bearing

风电机组桨轴承的早期故障率很高,变桨轴承故障诊断的一项关键工作是找到覆盖故障轴承信号的最佳频带,曲线图是一种用于表征信号中隐藏的非平稳性的高级技术。因此,允许对给定的问题作出响应。它包括确定谐振中心频率和使峰度最大化的适当带宽。本研究在分析了失效原因基础之上,提出了一种基于平方包络的光谱峰度法诊断变桨轴承打滑的方法。我们已经使用传动系风电机组的实际测量数据验证了光谱峰度诊断策略在改善单个缺陷诊断性能方面的潜力。

The early failure rate of propeller bearings in wind turbine units is very high.A key task in diagnosing pitch bearing faults is to find the optimal frequency band that covers the faulty bearing signal.Graph is an advanced technique used to characterize the non-stationary hidden in the signal.Therefore,it is allowed to respond to a given problem.It includes determining the resonant center frequency and maximizing the appropriate bandwidth for kurtosis.On the basis of analyzing the causes of failure,this article proposes a spectral kurtosis method based on square envelope for diagnosing pitch bearing slip.We have validated the potential of spectral kurtosis diagnosis strategies in improving the performance of individual defect diagnosis using actual measurement data from power train wind turbines.