基于时域和谱峭度特征融合及指数模型的滚动轴承RUL预测

RUL Prediction of Rolling Bearing Based on Fusion Feature of Time Domain and Spectral Kurtosis and Exponential Model

滚动轴承剩余使用寿命(RUL)预测对保障旋转机械设备平稳运行意义重大。针对时域特征预测精度波动大、数据利用率低等问题,提出一种基于时域和谱峭度特征融合及指数模型的滚动轴承RUL预测方法。从时域和谱峭度提取信号的特征进行平滑处理并基于单调性尺度排序,从而选取优势特征通过主成分分析(PCA)构建健康指标。然后,通过3σ准则确定退化点后对数据再处理。最后,基于贝叶斯理论和极大似然函数估计指数退化模型的参数来预测轴承每时刻的RUL,采用XJTU-SY数据集验证所提方法的有效性。结果表明:所提方法可根据当前观测轴承进行小样本数据潜在信息的挖掘,并能在强噪声背景下准确地表征非平稳信号的退化过程,提升RUL预测的精度。

Prediction of remaining useful life(RUL)of rolling bearings is of great significance to ensure smooth operation of rotating machinery.Aiming at the problems of large fluctuation of prediction accuracy and low data utilization rate of time domain features,a rolling bearing RUL prediction method was proposed based on the fusion feature of time domain and spectral kurtosis and exponential degradation model.The features of signals extracted from time domain and spectrum kurtosis were smoothed and sorted based on monotone scale,and then the dominant features were selected to construct health indicators through principal component analysis(PCA).Then,the degradation point was determined by the 3σcriterion and the data were reprocessed.Finally,the RUL of bearing was predicted at every moment based on Bayesian theory and maximum likelihood function estimation of exponential degradation model parameters.The XJTU-SY dataset were used to verify the effectiveness of the proposed method.The results show that the proposed method can mine potential information of small sample data based on current observation bearings and accurately characterize the degradation process of non-stationary signals under strong noise background,thus improving the prediction accuracy of RUL.