多元统计过程监控若干问题研究
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摘要
统计过程质量控制是一种监控及改善产品质量的有力工具,如果通过该方法能有效地检测、诊断工业过程中的故障,将带来巨大的经济效益。统计过程监控是一种基于多元统计理论的质量控制技术,通过对过程测量数据的分析和解释,可在线检测和识别过程中出现的异常工况。但将经典的统计投影方法直接应用于工业过程仍存在许多问题,论文对相应问题进行分析并取得了以下成果:
1、提出了以主角度(principal angles)的方式度量任意两个主元模型之间相似性的方法,并将其进行推广以便同时刻画一个主元模型与别的多个主元模型之间相似性的度量。并提出了一种多PCA(principal component analysis)模型过程监控方法及其在线应用的实现,包括多PCA模型合并策略、模型在线切换准则、模型更新及新模型在线添加方法。
2、提出了一种基于主角度的不同PLS(partial least squares)模型之间相似性的度量,并进一步提出了一种基于多PLS模型的过程监控方案。并分析了模型的在线应用、更新及新模型添加等问题。该方法可应用于多操作模式下的质量指标的估计和异常工况监控。
3、提出了PLS算法的一种等价形式,将PLS算法分解为投影及重构两个顺序进行但相对独立的步骤。以此为基础,提出了一种新的非线性PLS方法,通过两个连续的步骤可同时得到非线性隐变量及非线性重构。最后,从与PLS算法的一致性、隐变量之间的正交性、计算量大小和精度等不同角度对该新非线性PLS算法与已有算法进行了比较。
4、开发了具有一定通用性的过程统计监控软件平台,并已成功地将多PCA方法应用到常减压装置、催化裂化装置、大型乙烯裂解炉等工业过程的多元统计监控中。
Statistical process control (SPC) is one of the most powerful tools for process monitoring and product quality improvement. When applied to prompt detecting, diagnosing and appropriately coping with process malfunctions, it will be of considerable benefit. Statistical process monitoring (SPM) is based on multivariate statistical projection methodologies for online process malfunctions detection and diagnosis through analysis and interpretation of the collected measurements. Unfortunately, these conventional methods behave unsatisfactorily when applied to industrial processes. In this dissertation, this problem is addressed accordingly and the related achievements mainly include:1 the similarity metric of any two principal component analysis (PCA) models adopting the concept of principal angles, which is further extended to the case of more than two PCA models. A multiple PCA model based process monitoring methodology and its online applications are then presented, which include the algorithms for developing multiple PCA models, criterion for online models selection and methods for models updating and incorporation of new PCA models.2 the similarity metric of any two partial least squares (PLS) models, which is further incorporated into the framework of process monitoring using multiple PLS models. Issues including online application of the developed PLS models, updating and adding new models are discussed. This approach can be applied to product quality estimation and malfunction detection of processes with multiple operating modes.3 an equivalent presentation of PLS where PLS algorithm is achieved through two sequential but relatively independent steps including projection and reconstruction. A novel nonlinear PLS algorithm is then proposed where both nonlinear latent structures and nonlinear reconstruction are obtained straightforwardly through two consecutive steps. It is also compared with existing nonlinear PLS algorithms with respect to the consistency with PLS, the orthogonality of the latent structures and the computational requirements and accuracies.4, a general-purpose multivariate SPM software platform, where the multiple PCA based methodology is successfully applied to the monitoring of some industrial process including atmospheric distillation unit, fluidized analytic and cracking unit and
a large-scale ethylene pyrolysis furnace.
1、提出了以主角度(principal angles)的方式度量任意两个主元模型之间相似性的方法,并将其进行推广以便同时刻画一个主元模型与别的多个主元模型之间相似性的度量。并提出了一种多PCA(principal component analysis)模型过程监控方法及其在线应用的实现,包括多PCA模型合并策略、模型在线切换准则、模型更新及新模型在线添加方法。
2、提出了一种基于主角度的不同PLS(partial least squares)模型之间相似性的度量,并进一步提出了一种基于多PLS模型的过程监控方案。并分析了模型的在线应用、更新及新模型添加等问题。该方法可应用于多操作模式下的质量指标的估计和异常工况监控。
3、提出了PLS算法的一种等价形式,将PLS算法分解为投影及重构两个顺序进行但相对独立的步骤。以此为基础,提出了一种新的非线性PLS方法,通过两个连续的步骤可同时得到非线性隐变量及非线性重构。最后,从与PLS算法的一致性、隐变量之间的正交性、计算量大小和精度等不同角度对该新非线性PLS算法与已有算法进行了比较。
4、开发了具有一定通用性的过程统计监控软件平台,并已成功地将多PCA方法应用到常减压装置、催化裂化装置、大型乙烯裂解炉等工业过程的多元统计监控中。
Statistical process control (SPC) is one of the most powerful tools for process monitoring and product quality improvement. When applied to prompt detecting, diagnosing and appropriately coping with process malfunctions, it will be of considerable benefit. Statistical process monitoring (SPM) is based on multivariate statistical projection methodologies for online process malfunctions detection and diagnosis through analysis and interpretation of the collected measurements. Unfortunately, these conventional methods behave unsatisfactorily when applied to industrial processes. In this dissertation, this problem is addressed accordingly and the related achievements mainly include:1 the similarity metric of any two principal component analysis (PCA) models adopting the concept of principal angles, which is further extended to the case of more than two PCA models. A multiple PCA model based process monitoring methodology and its online applications are then presented, which include the algorithms for developing multiple PCA models, criterion for online models selection and methods for models updating and incorporation of new PCA models.2 the similarity metric of any two partial least squares (PLS) models, which is further incorporated into the framework of process monitoring using multiple PLS models. Issues including online application of the developed PLS models, updating and adding new models are discussed. This approach can be applied to product quality estimation and malfunction detection of processes with multiple operating modes.3 an equivalent presentation of PLS where PLS algorithm is achieved through two sequential but relatively independent steps including projection and reconstruction. A novel nonlinear PLS algorithm is then proposed where both nonlinear latent structures and nonlinear reconstruction are obtained straightforwardly through two consecutive steps. It is also compared with existing nonlinear PLS algorithms with respect to the consistency with PLS, the orthogonality of the latent structures and the computational requirements and accuracies.4, a general-purpose multivariate SPM software platform, where the multiple PCA based methodology is successfully applied to the monitoring of some industrial process including atmospheric distillation unit, fluidized analytic and cracking unit and
a large-scale ethylene pyrolysis furnace.
引文
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