基于非线性与非参数时间序列的SPC-EPC集成研究
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摘要
统计过程控制(SPC)与工程过程控制(EPC)是分别从过程工业和零件工业两个不同的领域发展而来的目的在于控制和提升产品质量的两种不同的技术。虽然它们达到各自目的的方法和途径不同，但是它们都以减小质量特性对目标值的偏移作为自己的目标。随着生产的发展和技术的进步，SPC-EPC集成逐渐被认为是一种更为有效的质量控制方法，得到了广泛的关注和应用。
随着生产过程和产品自身复杂程度的提高，越来越多的产品的质量特性在生产的过程中呈现出了超越线性自相关关系的复杂的非线性自相关关系。目前针对SPC-EPC集成的研究普遍采用线性时间序列来描述产品质量特性的自相关关系，这时线性模型在对于非线性自相关关系的描述上就会存在一定的偏差，从而影响最终的控制效果。故针对这一问题，本文提出了使用两种典型的非线性时间序列模型，即门限自回归模型(TAR)和平滑转移自回归模型(STAR)来对过程中的自相关关系进行描述，基于非线性时间序列模型建立控制器并进一步建立集成SPC-EPC控制体系。使用例子与大量模拟研究分析和验证了这一方法的控制效果。结果表明，基于非线性时间序列模型的集成SPC-EPC控制方法可以针对失控的复杂的非线性过程进行有效的控制。接着本文又基于一种更为高级的非参数时间序列模型，即函数系数自回归模型(FCAR)来描述系统的动态自相关关系，使用相同的方法对基于这一模型的SPC-EPC控制方法进行分析。结果表明，基于函数系数自回归模型的集成SPC-EPC控制方法可以针对失控的具体形式未知的复杂的非线性过程进行有效的控制。
目前针对SPC-EPC集成的研究普遍采用线性传递函数模型来描述产品生产过程中的输入输出关系，然而此种线性传递函数模型在对于更加贴近现代化生产过程的复杂非线性输入输出关系的描述上同样会出现一定的偏差。故针对这一问题，本文提出了使用非参数传递函数模型来描述这种更为复杂的输入输出关系。基于非参数传递函数模型建立控制器并进一步建立集成SPC-EPC控制体系，使用例子与大量模拟研究分析和验证了这一方法的控制效果。结果表明，基于非参数传递函数模型的集成SPC-EPC控制方法可以针对含有复杂的非线性输入输出关系的失控过程进行有效的控制。
Statistical process control (SPC) and engineering process control (EPC) are twotechniques originated from the process industry and parts industry. Although the pathsthey achieve their goals are different, they have the same goal of reducing thedeviation in the quality characteristics. With the development of modern productiontechniques, integrated SPC-EPC is now considered to be an effective quality controlmethod, and has received many attentions and applications.
Along with the development of the complexity in the production processes andthe products, more and more products have complex nonlinear autocorrelationships oftheir quality characteristics in their production processes. Now the studies ofintegrated SPC-EPC are based on linear time series model to describe theseautocorrelationships. But these linear models have errors in the description ofnonlinear relationships. This will affect the final control result. In order to solve thisproblem, this dissertation proposes a method using two typical kinds of nonlinear timeseries model——the threshold autoregressive model (TAR) and smooth transitionautoregressive model (STAR) to describe the autocorrelationships and buildingcontroller and integrated SPC-EPC system based on these two models. Theperformance of this control method is studied and verified through examples andsimulations. The results indicate that the method based on nonlinear time series modelcan effectively control the process which has nonlinear autocorrelationships. Thisdissertation then proposes a method based on a kind of more advanced time seriesmodel. That is the nonparametric functional coefficient autoregressive model (FCAR)to describe the dynamic nonlinear autocorrelationships. Using the same procedure tostudy this method, the results indicate that the method based on nonparametric timeseries model can effectively control the process which has complex nonlinearautocorrelationships.
Now the studies of integrated SPC-EPC are based on linear transfer functionmodel to describe the relationship between the input variables and output variables.But these linear transfer function models have errors in the description of nonlinearinput-output relationships, which are closer to modern manufacturing processes. Inorder to solve this problem, this dissertation proposes a method using thenonparametric transfer function model to describe the input-output relationships and building controller and integrated SPC-EPC system based on this model. Theperformance of this control method is studied and verified through examples andsimulations. The results indicate that the method based on nonparametric transferfunction model can effectively control the process which has nonlinear input-outputrelationships.
随着生产过程和产品自身复杂程度的提高，越来越多的产品的质量特性在生产的过程中呈现出了超越线性自相关关系的复杂的非线性自相关关系。目前针对SPC-EPC集成的研究普遍采用线性时间序列来描述产品质量特性的自相关关系，这时线性模型在对于非线性自相关关系的描述上就会存在一定的偏差，从而影响最终的控制效果。故针对这一问题，本文提出了使用两种典型的非线性时间序列模型，即门限自回归模型(TAR)和平滑转移自回归模型(STAR)来对过程中的自相关关系进行描述，基于非线性时间序列模型建立控制器并进一步建立集成SPC-EPC控制体系。使用例子与大量模拟研究分析和验证了这一方法的控制效果。结果表明，基于非线性时间序列模型的集成SPC-EPC控制方法可以针对失控的复杂的非线性过程进行有效的控制。接着本文又基于一种更为高级的非参数时间序列模型，即函数系数自回归模型(FCAR)来描述系统的动态自相关关系，使用相同的方法对基于这一模型的SPC-EPC控制方法进行分析。结果表明，基于函数系数自回归模型的集成SPC-EPC控制方法可以针对失控的具体形式未知的复杂的非线性过程进行有效的控制。
目前针对SPC-EPC集成的研究普遍采用线性传递函数模型来描述产品生产过程中的输入输出关系，然而此种线性传递函数模型在对于更加贴近现代化生产过程的复杂非线性输入输出关系的描述上同样会出现一定的偏差。故针对这一问题，本文提出了使用非参数传递函数模型来描述这种更为复杂的输入输出关系。基于非参数传递函数模型建立控制器并进一步建立集成SPC-EPC控制体系，使用例子与大量模拟研究分析和验证了这一方法的控制效果。结果表明，基于非参数传递函数模型的集成SPC-EPC控制方法可以针对含有复杂的非线性输入输出关系的失控过程进行有效的控制。
Statistical process control (SPC) and engineering process control (EPC) are twotechniques originated from the process industry and parts industry. Although the pathsthey achieve their goals are different, they have the same goal of reducing thedeviation in the quality characteristics. With the development of modern productiontechniques, integrated SPC-EPC is now considered to be an effective quality controlmethod, and has received many attentions and applications.
Along with the development of the complexity in the production processes andthe products, more and more products have complex nonlinear autocorrelationships oftheir quality characteristics in their production processes. Now the studies ofintegrated SPC-EPC are based on linear time series model to describe theseautocorrelationships. But these linear models have errors in the description ofnonlinear relationships. This will affect the final control result. In order to solve thisproblem, this dissertation proposes a method using two typical kinds of nonlinear timeseries model——the threshold autoregressive model (TAR) and smooth transitionautoregressive model (STAR) to describe the autocorrelationships and buildingcontroller and integrated SPC-EPC system based on these two models. Theperformance of this control method is studied and verified through examples andsimulations. The results indicate that the method based on nonlinear time series modelcan effectively control the process which has nonlinear autocorrelationships. Thisdissertation then proposes a method based on a kind of more advanced time seriesmodel. That is the nonparametric functional coefficient autoregressive model (FCAR)to describe the dynamic nonlinear autocorrelationships. Using the same procedure tostudy this method, the results indicate that the method based on nonparametric timeseries model can effectively control the process which has complex nonlinearautocorrelationships.
Now the studies of integrated SPC-EPC are based on linear transfer functionmodel to describe the relationship between the input variables and output variables.But these linear transfer function models have errors in the description of nonlinearinput-output relationships, which are closer to modern manufacturing processes. Inorder to solve this problem, this dissertation proposes a method using thenonparametric transfer function model to describe the input-output relationships and building controller and integrated SPC-EPC system based on this model. Theperformance of this control method is studied and verified through examples andsimulations. The results indicate that the method based on nonparametric transferfunction model can effectively control the process which has nonlinear input-outputrelationships.
引文
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