为了提高双直线电动机驱动的同步直接进给轴的运动精度,对该类直接进给轴的全行程热误差在线补偿方法进行了研究。分析了双直接进给轴全行程热误差的影响因素,提出一种基于核偏最小二乘法(Kernel partial least squares,KPLS)和模糊逻...为了提高双直线电动机驱动的同步直接进给轴的运动精度,对该类直接进给轴的全行程热误差在线补偿方法进行了研究。分析了双直接进给轴全行程热误差的影响因素,提出一种基于核偏最小二乘法(Kernel partial least squares,KPLS)和模糊逻辑相结合的双直接进给轴全行程热误差的在线补偿方法。应用激光干涉仪测量其热变形量,使用热电偶和红外测温仪测量进给机构关键点的温度,以时间匹配温度和变形量数据建立统计样本,在均匀离散点位置建立热误差KPLS识别模型,通过在线计算得到离散点热误差补偿量,再根据任意位置与离散点的模糊关联程度,综合计算全行程任意位置处热误差补偿量。以此理论为基础,建立补偿决策函数和补偿系统,依据补偿决策函数智能推断补偿值,通过向数控系统发送补偿码实现在线补偿。在自构建的龙门双直线电动机驱动的直接进给轴平台上,进行全行程热误差在线补偿试验研究,结果表明:混合KPLS与模糊逻辑可以有效的对双直接进给轴全行程热误差在线补偿,经过随机测试验证,补偿后的进给精度提高了50%。展开更多
针对过程非线性、基于历史数据构建的离线模型泛化性差以及基于滑动窗口和每样本递推更新的在线建模方法难以均衡建模精度和建模速度等问题,提出了一种在线核偏最小二乘(On-line kernel partial least squares,OLKPLS)建模方法.该方法...针对过程非线性、基于历史数据构建的离线模型泛化性差以及基于滑动窗口和每样本递推更新的在线建模方法难以均衡建模精度和建模速度等问题,提出了一种在线核偏最小二乘(On-line kernel partial least squares,OLKPLS)建模方法.该方法依据新样本与建模样本间的近似线性依靠(Approximate linear dependence,ALD)值和代表工业过程特性漂移幅度的阈值,选择有价值样本更新KPLS模型,并采用合成数据和Benchmark平台数据对该方法进行了仿真验证.针对基于离线历史数据建立的融合多传感器信息的磨机负荷参数集成模型难以适应磨矿过程时变特性的问题,提出了基于OLKPLS和在线自适应加权融合算法的在线集成建模方法,并通过实验球磨机的实际运行数据仿真验证了方法的有效性.展开更多
A novel thickness measurement method for surface insulation coating of silicon steel based on NIR spectrometry is explored.The NIR spectra of insulation coating of silicon steel were collected by acousto-optic tunable...A novel thickness measurement method for surface insulation coating of silicon steel based on NIR spectrometry is explored.The NIR spectra of insulation coating of silicon steel were collected by acousto-optic tunable filter(AOTF) NIR spectrometer.To make full use of the effective information of NIR spectral data,discrete binary particle swarm optimization(DBPSO) algorithm was used to select the optimal wavelength variates.The new spectral data,composed of absorbance at selected wavelengths,were used to create the thickness quantitative analysis model by kernel partial least squares(KPLS) algorithm coupled with Boosting.The results of contrast experiments showed that the Boosting-KPLS model could efficiently improve the analysis accuracy and speed.It indicates that Boosting-KPLS is a more accurate and robust analysis method than KPLS for NIR spectral analysis.The maximal and minimal absolute error of 30 testing samples is respectively-0.02 μm and 0.19 μm,and the maximal relative error is 14.23%.These analysis results completely meet the practical measurement need.展开更多
文摘为了提高双直线电动机驱动的同步直接进给轴的运动精度,对该类直接进给轴的全行程热误差在线补偿方法进行了研究。分析了双直接进给轴全行程热误差的影响因素,提出一种基于核偏最小二乘法(Kernel partial least squares,KPLS)和模糊逻辑相结合的双直接进给轴全行程热误差的在线补偿方法。应用激光干涉仪测量其热变形量,使用热电偶和红外测温仪测量进给机构关键点的温度,以时间匹配温度和变形量数据建立统计样本,在均匀离散点位置建立热误差KPLS识别模型,通过在线计算得到离散点热误差补偿量,再根据任意位置与离散点的模糊关联程度,综合计算全行程任意位置处热误差补偿量。以此理论为基础,建立补偿决策函数和补偿系统,依据补偿决策函数智能推断补偿值,通过向数控系统发送补偿码实现在线补偿。在自构建的龙门双直线电动机驱动的直接进给轴平台上,进行全行程热误差在线补偿试验研究,结果表明:混合KPLS与模糊逻辑可以有效的对双直接进给轴全行程热误差在线补偿,经过随机测试验证,补偿后的进给精度提高了50%。
文摘针对过程非线性、基于历史数据构建的离线模型泛化性差以及基于滑动窗口和每样本递推更新的在线建模方法难以均衡建模精度和建模速度等问题,提出了一种在线核偏最小二乘(On-line kernel partial least squares,OLKPLS)建模方法.该方法依据新样本与建模样本间的近似线性依靠(Approximate linear dependence,ALD)值和代表工业过程特性漂移幅度的阈值,选择有价值样本更新KPLS模型,并采用合成数据和Benchmark平台数据对该方法进行了仿真验证.针对基于离线历史数据建立的融合多传感器信息的磨机负荷参数集成模型难以适应磨矿过程时变特性的问题,提出了基于OLKPLS和在线自适应加权融合算法的在线集成建模方法,并通过实验球磨机的实际运行数据仿真验证了方法的有效性.
基金National High Technology Research and Development Program of China(2009AA04Z131)Natural Science Foundation of China (50877056)
文摘A novel thickness measurement method for surface insulation coating of silicon steel based on NIR spectrometry is explored.The NIR spectra of insulation coating of silicon steel were collected by acousto-optic tunable filter(AOTF) NIR spectrometer.To make full use of the effective information of NIR spectral data,discrete binary particle swarm optimization(DBPSO) algorithm was used to select the optimal wavelength variates.The new spectral data,composed of absorbance at selected wavelengths,were used to create the thickness quantitative analysis model by kernel partial least squares(KPLS) algorithm coupled with Boosting.The results of contrast experiments showed that the Boosting-KPLS model could efficiently improve the analysis accuracy and speed.It indicates that Boosting-KPLS is a more accurate and robust analysis method than KPLS for NIR spectral analysis.The maximal and minimal absolute error of 30 testing samples is respectively-0.02 μm and 0.19 μm,and the maximal relative error is 14.23%.These analysis results completely meet the practical measurement need.
