针对高精度伺服机构压装质量控制难度大的问题,提出了一种基于离群数据检测和线性回归的智能质量预警方法。采用线性回归分析装配质量与压装过程之间的关系,建立了压装的“位移-力”数学模型,并定义了合格的压装力范围对装配质量进行控...针对高精度伺服机构压装质量控制难度大的问题,提出了一种基于离群数据检测和线性回归的智能质量预警方法。采用线性回归分析装配质量与压装过程之间的关系,建立了压装的“位移-力”数学模型,并定义了合格的压装力范围对装配质量进行控制。为了对压装过程中的“位移-力”原始数据集进行预处理,本文设计了一种改进的基于区域密度和P权值的局部离群因子(Local outlier factor based on area density and P weight,LAOPW)检测算法,以剔除导致线性回归数学模型不准确的离群值。该算法引入了基于信息熵的加权距离进行距离度量,并用P权值代替可达距离。实验结果表明,该算法在检测效率上比传统的局部离群因子(Local outlier factor,LOF)算法提高了5.6 ms,而检测准确率比基于区域密度的局部离群因子(Local outlier factor based on area density,LAOF)算法改善了2%左右。将本文提出的LAOPW算法和线性回归模型应用于高精度伺服机构压装质量控制,能够有效进行压装质量智能预警。展开更多
In order to understand energy consumption and ensure precise load prediction,it is essential to identify the variation of gas consumption in response to ambient temperature change outdoor.In this paper,the relationshi...In order to understand energy consumption and ensure precise load prediction,it is essential to identify the variation of gas consumption in response to ambient temperature change outdoor.In this paper,the relationship is identified by using Empirical Mode Decomposition(EMD)and linear regression analysis together with outlier detection.EMD is a data processing tool that can divide original data into several Intrinsic Mode Functions(IMFs)with a lower frequency residue.By applying the data mining technique-Mahalanobis distance measurement,some outliers from real-time gas consumption and temperature data points are detected,which are excluded from the data sets to ensure accuracy.Correlation coefficients between the gas load and ambient temperature are calculated and denoted as an important index to quantify their relationship through regression analysis.By comparing such indices on realtime data and EMD processed data,the weather-sensitive part of gas demand is identified.The methods are implemented on a local energy system and the results reveal that the outcome after EMD presents a higher level of correlation between the gas load and ambient temperature,compared to the results from directly using the real-time gas load and temperature data.展开更多
文摘针对高精度伺服机构压装质量控制难度大的问题,提出了一种基于离群数据检测和线性回归的智能质量预警方法。采用线性回归分析装配质量与压装过程之间的关系,建立了压装的“位移-力”数学模型,并定义了合格的压装力范围对装配质量进行控制。为了对压装过程中的“位移-力”原始数据集进行预处理,本文设计了一种改进的基于区域密度和P权值的局部离群因子(Local outlier factor based on area density and P weight,LAOPW)检测算法,以剔除导致线性回归数学模型不准确的离群值。该算法引入了基于信息熵的加权距离进行距离度量,并用P权值代替可达距离。实验结果表明,该算法在检测效率上比传统的局部离群因子(Local outlier factor,LOF)算法提高了5.6 ms,而检测准确率比基于区域密度的局部离群因子(Local outlier factor based on area density,LAOF)算法改善了2%左右。将本文提出的LAOPW算法和线性回归模型应用于高精度伺服机构压装质量控制,能够有效进行压装质量智能预警。
文摘In order to understand energy consumption and ensure precise load prediction,it is essential to identify the variation of gas consumption in response to ambient temperature change outdoor.In this paper,the relationship is identified by using Empirical Mode Decomposition(EMD)and linear regression analysis together with outlier detection.EMD is a data processing tool that can divide original data into several Intrinsic Mode Functions(IMFs)with a lower frequency residue.By applying the data mining technique-Mahalanobis distance measurement,some outliers from real-time gas consumption and temperature data points are detected,which are excluded from the data sets to ensure accuracy.Correlation coefficients between the gas load and ambient temperature are calculated and denoted as an important index to quantify their relationship through regression analysis.By comparing such indices on realtime data and EMD processed data,the weather-sensitive part of gas demand is identified.The methods are implemented on a local energy system and the results reveal that the outcome after EMD presents a higher level of correlation between the gas load and ambient temperature,compared to the results from directly using the real-time gas load and temperature data.
