基于PCA-UPF的风力机轴承剩余寿命预测方法

为解决风力机轴承退化指标提取困难与剩余寿命预测精度低的问题,提出一种基于主成分分析(PCA)和无迹粒子滤波(UPF)的预测方法.该方法主要包括退化指标提取和寿命预测2个步骤.在退化指标提取部分,通过PCA对轴承实时振动信号的多域原始特征集进行融合,得到能够反映轴承衰退趋势的退化指标.在剩余寿命预测部分,通过对轴承历史数...

PREDICTION APPROACH OF REMAINING USEFUL LIFE FOR WIND TURBINE BEARINGS BASED ON PCA-UPF

In order to solve the difficult problem of extracting degradation indicator of wind turbine bearings and the low prediction accuracy of remaining useful life(RUL),a new prediction approach based on principal component analysis(PCA)and unscented particle filter(UPF)is proposed.The approach includes two steps:indicator extraction and RUL prediction.In the step of indicator extraction,original feature sets of real-time vibration signal of bearing are fused by using PCA approach.The degradation indicator which can reflect the deterioration trend of bearing is obtained.In the step of RUL prediction,a degradation model is constructed by fitting and analyzing the historical data of the bearing,the model parameters are updated by UPF to realize the degradation state tracking and prediction.Real test data of wind turbine bearings are involved to demonstrate the effectiveness of the proposed technique.Compared with PF,the proposed approach shows its superiority in particle degeneracy problem reduction and RUL prediction,which provides a reference for the health management and reliability evaluation of large wind turbines.