基于NLMS和Autoformer的滚动轴承RUL预测  

RUL Prediction of Rolling Bearing Based on NLMS and Autoformer

作　　者：刘康宁 徐遵义[1] 李晨 闫春相 LIU Kang-ning;XU Zun-yi;LI Chen;YAN Chun-xiang(School of Computer Science and Technology,Shandong Jianzhu University,Jinan 250101,China)

机构地区：[1]山东建筑大学计算机科学与技术学院,山东济南250101

出　　处：《计算机技术与发展》2024年第3期177-184,共8页Computer Technology and Development

基　　金：山东省重点研发计划(重大科技创新工程)(2021CXGC011204)。

摘　　要：准确预测滚动轴承剩余使用寿命(Remaining Useful Life,RUL)对维护建筑机械设备稳定运行、保障生产安全具有重要的现实需求和应用价值。为提升滚动轴承RUL预测准确率,提出一种基于归一化最小均方(Normalized Least Mean Square,NLMS)自适应滤波器和Autoformer长序列预测模型的滚动轴承RUL预测新方法。使用NLMS自适应滤波器对滚动轴承原始振动信号进行降噪,从降噪振动信号中分段提取初始时域特征,采用Spearman相关系数进行特征筛选,经归一化后形成多维特征集;利用Autoformer模型中序列分解模块与自相关机制建立多维特征集与滚动轴承RUL之间的分段非线性映射,实现滚动轴承RUL预测;在PHM 2012数据集与XJTU-SY数据集上进行对比实验,结果表明该方法与已有方法相比可取得最低预测误差,均方根误差(Root Mean Squared Error,RMSE)与平均绝对误差(Mean Absolute Error,MAE)分别提升24.4%与47.2%,证明了该方法在滚动轴承RUL预测的有效性。For maintaining the stable operation of construction machinery equipment and ensuring production safety,accurately predicting Remaining Useful Life(RUL)of rolling bearing has significant practical needs and application value.Based on Normalized Least Mean Square(NLMS)adaptive filter and long-term series forecasting model Autoformer,a new method was proposed in order to improve the RUL prediction accuracy of rolling bearing.The NLMS adaptive filter was used to denoise the original vibration signal of rolling bearing.The Spearman correlation coefficient was used for feature selection from the denoised signal,and the multidimensional feature set was created.In order to forecast the rolling bearing RUL,the series decomposition block and auto-correlation mechanism in the Autoformer model were used to establish the piecewise mapping relationship between the multi-dimensional feature sets and the rolling bearing RUL.Experimental results on the PHM 2012 dataset and XJTU-SY dataset showed that the proposed method could achieve the lowest prediction error compared with the existing methods,and the Root Mean Squared Error(RMSE)and Mean Absolute Error(MAE)performance were improved by 24.4%and 47.2%respectively,which proved the effectiveness of the proposed method in the field of rolling bearing RUL prediction.

关 键 词：滚动轴承 剩余使用寿命预测 Autoformer模型 NLMS自适应滤波器 数据预处理 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TH133.33[自动化与计算机技术—控制科学与工程]