针对多变量预测模型模式识别方法中的最小二乘拟合可能出现病态的问题,提出了基于岭回归的多变量预测模型(Ridge regression-Variable Predictive Model based Class Discriminate,RVPMCD)分类方法,该方法通过引入岭参数,降低其均方拟...针对多变量预测模型模式识别方法中的最小二乘拟合可能出现病态的问题,提出了基于岭回归的多变量预测模型(Ridge regression-Variable Predictive Model based Class Discriminate,RVPMCD)分类方法,该方法通过引入岭参数,降低其均方拟合误差,减小自变量间复共线性关系对参数估计的影响,改善了原方法中最小二乘回归拟合参数失真的现象,从而有望建立更加准确的预测模型。对滚动轴承的振动信号提取特征值,组成特征向量,采用RVPMCD方法对训练样本建立预测模型,利用RVPMCD所建立的预测模型进行模式识别。实验分析结果表明,基于岭回归的多变量预测模型分类方法可以更有效地对滚动轴承的工作状态和故障类型进行识别。展开更多
A novel systematic quality monitoring and prediction method based on Fisher discriminant analysis (FDA) and kernel regression is proposed. The FDA method is first used for quality monitoring. If the process is un-der ...A novel systematic quality monitoring and prediction method based on Fisher discriminant analysis (FDA) and kernel regression is proposed. The FDA method is first used for quality monitoring. If the process is un-der normal condition, then kernel regression is further used for quality prediction and estimation. If faults have oc-curred, the contribution plot in the fault feature direction is used for fault diagnosis. The proposed method can ef-fectively detect the fault and has better ability to predict the response variables than principle component regression (PCR) and partial least squares (PLS). Application results to the industrial fluid catalytic cracking unit (FCCU) show the effectiveness of the proposed method.展开更多
Porosity is one of the most important properties of oil and gas reservoirs. The porosity data that come from well log are only available at well points. It is necessary to use other method to estimate reservoir porosi...Porosity is one of the most important properties of oil and gas reservoirs. The porosity data that come from well log are only available at well points. It is necessary to use other method to estimate reservoir porosity.Seismic data contain abundant lithological information. Because there are inherent correlations between reservoir property and seismic data,it is possible to estimate reservoir porosity by using seismic data and attributes.Probabilistic neural network is a powerful tool to extract mathematical relation between two data sets. It has been used to extract the mathematical relation between porosity and seismic attributes. Firstly,a seismic impedance volume is calculated by seismic inversion. Secondly,several appropriate seismic attributes are extracted by using multi-regression analysis. Then a probabilistic neural network model is trained to obtain a mathematical relation between porosity and seismic attributes. Finally,this trained probabilistic neural network model is implemented to calculate a porosity data volume. This methodology could be utilized to find advantageous areas at the early stage of exploration. It is also helpful for the establishment of a reservoir model at the stage of reservoir development.展开更多
文摘针对多变量预测模型模式识别方法中的最小二乘拟合可能出现病态的问题,提出了基于岭回归的多变量预测模型(Ridge regression-Variable Predictive Model based Class Discriminate,RVPMCD)分类方法,该方法通过引入岭参数,降低其均方拟合误差,减小自变量间复共线性关系对参数估计的影响,改善了原方法中最小二乘回归拟合参数失真的现象,从而有望建立更加准确的预测模型。对滚动轴承的振动信号提取特征值,组成特征向量,采用RVPMCD方法对训练样本建立预测模型,利用RVPMCD所建立的预测模型进行模式识别。实验分析结果表明,基于岭回归的多变量预测模型分类方法可以更有效地对滚动轴承的工作状态和故障类型进行识别。
基金Supported by the National Natural Science Foundation of China (60504033)the Open Project of State Key Laboratory of Industrial Control Technology in Zhejiang University (0708004)
文摘A novel systematic quality monitoring and prediction method based on Fisher discriminant analysis (FDA) and kernel regression is proposed. The FDA method is first used for quality monitoring. If the process is un-der normal condition, then kernel regression is further used for quality prediction and estimation. If faults have oc-curred, the contribution plot in the fault feature direction is used for fault diagnosis. The proposed method can ef-fectively detect the fault and has better ability to predict the response variables than principle component regression (PCR) and partial least squares (PLS). Application results to the industrial fluid catalytic cracking unit (FCCU) show the effectiveness of the proposed method.
文摘Porosity is one of the most important properties of oil and gas reservoirs. The porosity data that come from well log are only available at well points. It is necessary to use other method to estimate reservoir porosity.Seismic data contain abundant lithological information. Because there are inherent correlations between reservoir property and seismic data,it is possible to estimate reservoir porosity by using seismic data and attributes.Probabilistic neural network is a powerful tool to extract mathematical relation between two data sets. It has been used to extract the mathematical relation between porosity and seismic attributes. Firstly,a seismic impedance volume is calculated by seismic inversion. Secondly,several appropriate seismic attributes are extracted by using multi-regression analysis. Then a probabilistic neural network model is trained to obtain a mathematical relation between porosity and seismic attributes. Finally,this trained probabilistic neural network model is implemented to calculate a porosity data volume. This methodology could be utilized to find advantageous areas at the early stage of exploration. It is also helpful for the establishment of a reservoir model at the stage of reservoir development.
