基于TFER及退化趋势相似性分析的轴承剩余使用寿命预测

Bearing remaining using life prediction method based on TFER and degradation trend similarity analysis

为了解决传统退化指标对轴承退化起始点的敏感性差,以及退化指标趋势偏移导致无法准确预测风力机轴承剩余使用寿命(RUL)的问题,提出了一种基于Teager能量算子-故障能量比(TFER)与退化趋势(DT)相似性检测相结合的双指数轴承RUL预测方法。首先,通过计算原始信号的TFER值,根据4σ原则确定轴承退化起始点,提取了TFER值趋势特征作为退化指标;然后,采用历史TFER值拟合双指数退化模型,通过分析最新TFER值与拟合曲线的相似性,选取了最佳DT段;最后,通过外推相似性最佳的DT段至失效阈值,进行了风力机轴承RUL预测。实验结果表明:该预测方法对退化起始时间点的检测精度达到98%,与原始指数模型、长短期记忆神经网络(LSTM)以及支持向量回归(SVR)相比,该方法在轴承RUL预测精度上分别提高了10.04%、6.29%、5.22%。该方法不仅提升了风力机轴承的预测性维护精度,还对降低运营成本和提高维护效率提供了有力支撑。

To address the challenges posed by the poor sensitivity of traditional degradation indices to the onset of bearing degradation and the inability to accurately predict the remaining using life(RUL)of wind turbine bearings due to trend shifts in degradation indices,a bi-exponential method for predicting bearing RUL was proposed.This method was based on the combination of Teager s energy arithmetic-ratio of failures to energy(TFER)and degradation trend(DT)similarity detection.Firstly,the TFER value of the original signal was calculated to determine the bearing degradation starting point following the 4σprinciple.The trend feature of the TFER value was then extracted as a degradation indicator.Subsequently,a bi-exponential degradation model was fitted using historical TFER values.Then,the best DT segment was chosen by analyzing the similarity between the latest TFER values and the fitted curve.Finally,the wind turbine bearing RUL was predicted by extrapolating the DT segment with the best similarity to the failure threshold.The experimental results show that the prediction method achieves 98%accuracy in detecting the degradation onset time point,and improves 10.04%,6.29%,and 5.22%in bearing RUL prediction accuracy compared with the original exponential model,the long-and short-term memory neural network(LSTM),and the support vector regression(SVR),respectively.This method not only improves the accuracy of predictive maintenance of wind turbine bearings,but also provides strong support for reducing operating costs and improving maintenance efficiency.