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...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。展开更多
A Matrix Inversion Normalized Least Mean Square (MI-NLMS) adaptive beamforming algorithm was developed for smart antenna application. The MI-NLMS which combined the individual good aspects of Sample Matrix Inversion (...A Matrix Inversion Normalized Least Mean Square (MI-NLMS) adaptive beamforming algorithm was developed for smart antenna application. The MI-NLMS which combined the individual good aspects of Sample Matrix Inversion (SMI) and the Normalized Least Mean Square (NLMS) algorithms is described. Simulation results showed that the less complexity MI-NLMS yields 15 dB improvements in interference suppression and 5 dB gain enhancement over LMS algorithm, converges from the initial iteration and achieves 24% BER improvements at cochannel interference equal to 5. For the case of 4-element uniform linear array antenna, MI-NLMS achieved 76% BER reduction over LMS algorithm.展开更多
文摘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。
基金Project supported by the IRPA Secretariat, Ministry of Science,Technology and Environment of Malaysia (No. 04-02-02-0029) andthe Zamalah Scheme
文摘A Matrix Inversion Normalized Least Mean Square (MI-NLMS) adaptive beamforming algorithm was developed for smart antenna application. The MI-NLMS which combined the individual good aspects of Sample Matrix Inversion (SMI) and the Normalized Least Mean Square (NLMS) algorithms is described. Simulation results showed that the less complexity MI-NLMS yields 15 dB improvements in interference suppression and 5 dB gain enhancement over LMS algorithm, converges from the initial iteration and achieves 24% BER improvements at cochannel interference equal to 5. For the case of 4-element uniform linear array antenna, MI-NLMS achieved 76% BER reduction over LMS algorithm.
