摘要
针对传统机器学习出现"伪回归"问题,忽视非平稳序列间的长期依赖关系,提出协整理论和向量误差修正模型预测滚动轴承的性能退化趋势,进而预测其剩余使用寿命(RUL)。首先从振动信号中提取峭度值、峰峰值和均方根值,并分析其平稳性。然后检验3种时域特征的协整关系,根据检验结果来建立向量误差修正模型,并对模型进行残差分析,分析结果接近于高斯白噪声分布,表明模型良好。最后利用该模型预测轴承性能退化趋势和RUL,并使用均方根误差(RMSE)、平均百分比误差(MPE)、平均绝对百分比误差(MAPE)和Theil不等系数来定量评估预测结果。实验结果表明,提出的向量误差修正模型相比于差分平均移动自回归-卡尔曼滤波模型,具有更高的预测精度,并简化了建模过程。
Aiming at the problem of“pseudo regression”in traditional machine learning,ignoring the long-term dependence between non-stationary sequences,the cointegration theory and vector error correction model were proposed to predict the performance degradation trend of rolling bearings,and then predicted the remaining useful life(RIL).Firstly,extracted the kurtosis value,peak-to-peak value and root mean square value from the vibration signal,and analyzed its stability.Then,tested the cointegration relationship of the time domain features and established vector error correction models based on the test results,and performed the residual analysis.The analysis results were close to the Gaussian white noise distribution,indicating that the models were good.Finally,used the models to predict the bearing performance degradation trend and applied RUL,and RMSE,MPE,MAPE and the unequal coefficients of Theil to quantitatively evaluate the prediction results.Experimental results show that the proposed vector error correction model has higher prediction accuracy than the differential average moving autoregressive-Kalman filter model,and simplifies the modeling process.
作者
杜望
王衍学
Du Wang;Wang Yanxue(Guilin University of Electronic Technology,Guilin 541004,China;Beijing University of Architecture,Beijing 100044,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2020年第9期32-39,共8页
Journal of Electronic Measurement and Instrumentation
基金
国家自然科学基金(面上)项目(51875032)
桂林电子科技大学研究生教育创新计划项目(2020YCXS014)资助。
关键词
滚动轴承
协整理论
向量误差修正模型
剩余寿命预测
roller bearing
cointegration system
vector error correction model
remaining useful life prediction
