复杂生产过程质量控制的智能方法研究
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摘要
质量作为社会经济发展的关键因素之一,越来越受到社会各界的重视。随着科学技术的发展,实现日渐复杂生产过程的质量管理成为研究者们关注的重大课题。质量管理是根据所制定的质量方针,通过质量策划、质量控制、质量保证和质量改进来实现产品或服务的全部活动。过程质量控制作为质量控制的关键部分,是实现产品创造的主要阶段,因而保证过程质量是实现质量管理的一种有效途径。随着现代生产过程的日益复杂,其过程具有数据高维、非线性,过程模型不确定和各子过程相互干扰并呈现强耦合等特点,因而比传统生产过程质量控制更为困难。本文通过复杂系统理论结合多元化、智能化等方向发展的质量控制方法,实现复杂生产过程的质量控制。
支持向量机是建立在统计学习理论的VC维理论和最小结构风险基础上的一种机器学习智能方法。通过核函数实现非线性问题向线性问题的转化,能较好的解决小样本、非线性、高维数、局部最小等问题,具有很强的泛化能力。粒子群算法模拟鸟群捕食行为,其算法概念简单,控制参数少,易于实现,同时兼有进化计算和群智能优化的特点,通过个体间的协作与竞争,实现对复杂空间最优解的搜索。粒子群算法是解决整数非线性优化问题、非线性连续优化问题和组合优化问题等方面的有效优化工具。本文利用支持向量机作为建立复杂生产过程质量模型的工具,将粒子群算法及其改进算法应用于支持向量机参数优化、模型最优解选择等问题中,从而实现复杂生产过程的质量控制。
针对复杂生产过程故障检测问题,本文将小波包分析方法作为过程样本数据的消噪工具,通过多层次划分逼近原信号的方法消除样本数据中的噪音和干扰,提取高效的数据样本,建立基于核主元分析的多变量统计过程监控模型,实现非线性问题向线性问题的转化,从而进行复杂生产过程的故障检测。本文通过1个数值算例和TE复杂化工过程故障分析进行故障检测研究,并与PCA和KPCA故障检测方法进行对比研究,证明所提出方法的可行性和有效性。
控制图作为统计过程控制的有效工具,控制图模式的识别在处理复杂生产过程质量问题中发挥着重要的作用。针对目前识别较难的控制图混合模式,本文首先提取样本数据的统计特征和形状特征值作为质量信息特征值,再利用主元分析方法进行第二次特征提取,得到高效的样本数据信息。利用支持向量机泛化能力强、识别精度高的特点实现控制图模式的多分类识别,同时采取自适应变异粒子群算法进行多分类支持向量机的参数优化。本文提出利用六种控制图基本模式和四种控制图混合模式对所提出的模型进行分析,通过与神经网络、支持向量机和主元分析-支持向量机这三种方法的对比试验,结果表明本文所建立的控制图模式识别方法能取得更高的分类识别率,为控制图模式识别研究提供一定的参考价值。
针对复杂生产过程的多输入、多输出、非线性的特点,提出了一种基于局部模型的多工况过程质量预测方法。首先利用K均值聚类方法进行复杂数据处理,实现复杂生产过程工况的划分,利用所需样本小、学习能力强的支持向量机回归原理建立各工况下的局部模型,再利用自适应粒子群算法求得各局部模型最优加权值,建立预测全局多模型,从而实现复杂生产过程的质量预测。通过TE过程的正常模式这一复杂过程对所建立的多模型预测方法进行仿真预测,并与局部模型、BP神经网络模型和支持向量机模型进行对比研究,结果表明本模型的预测效果相对其他预测方法较好,相对误差控制在1%左右,同时表明其预测方法的可行性和有效性。
复杂生产过程很难用单纯抽象的数学模型进行描述,生产商希望通过虚拟仿真的方式动态模拟生产过程,对生产过程的各质量因素进行分析,得到影响其质量问题的因素。本文在沥青混合料生产过程的级配控制的数学模型框架下,利用Arena仿真软件建立人机交互式界面,根据实际生产情况进行参数设定,模拟特定情况下的沥青混合料生产情况。并通过Arena软件内嵌的编译模块,编写不同的控制方法,实现沥青混合料质量控制的多因素控制逻辑,并利用图像输出实时观察生产状态的变化。模型试验结果表明系统优化控制与控制策略相关,适合的控制策略能使系统大幅度降低质量问题,仿真控制方法为沥青混合料级配的质量控制提供了很好的途径,极大地缩短产品的试验时间,及时控制级配偏差,有利于提高产品的整体质量,表明虚拟过程质量控制方法是复杂生产过程质量控制的一种有效途径。
本文利用支持向量机、粒子群算法、核主元分析、小波包分析等智能方法以及仿真技术进行复杂生产过程质量控制研究,主要从故障检测、控制图模式识别、质量预测和虚拟控制四个方面实现复杂生产过程质量的智能控制。文中所提的方法具有较高的理论价值和生产实践意义,并为后续的研究奠定了坚实的基础。
Quality, as one of the essential factors of the socio-economic development, has been got more and more people's attention. With the development of science and technology, the quality management of increasingly complex production processes has become a major subject of the researchers. Quality management implements all activities of the products or services of quality planning, quality control, quality assurance and quality improvement, which are based on the formulation of quality policy. Process quality control, as a key part of the quality control, is the main stage of the product creation and an effective way to achieve quality management to ensure process quality. With increasingly complex production process, the process has the characteristics of high-dimensional and non-linear data, the uncertainty process model, sub-process interfere with each other and strong coupling, its quality control is more difficult than the traditional production process. In this paper, the complex system theory combined with the diversified, intelligent quality control methods are used to realize the quality control of the complexity production process.
Support vector machine (SVM) is an intelligent method of machine learning based on VC-dimensional theory of statistical learning theory and structural risk minimization. SVM uses the kernel function to make the nonlinear problem transform to linear problem, which makes it better to solve the small samples, nonlinearity, high dimension and local minima problems, and has greater generalization ability. Particle swarm optimization (PSO) algorithm simulates birds'predatory behavior to search the optimal solution of complex space by the collaboration and competition between individuals, which has the characteristics of simple concept, few control parameters, easy to implement, evolutionary computation and swarm intelligence optimization. PSO is an effective optimization tool to solve integer nonlinear optimization, nonlinear continuous optimization problems and combinatorial optimization problems. In this paper, support vector machine as a tool to create complex production process quality model, the particle swarm algorithm and its improved algorithm are adopted to optimize the parameters of support vector machine and select model optimal solution, so that implement the quality control of complex production process.
The fault detection of complex production process is a significant problem of quality control. In this paper, wavelet packet transform is chosen as the denoising tools of the process sample data, which uses a multi-hierarchical division method to eliminate the noise and interference of sample data to get the approximation of the original signal, so that extracts the effective data samples. And then, multivariate statistical process monitoring model, which use kernel principal component analysis to make the nonlinear problem transform to linear problem, is proposed to realize the fault detection of complex process. A numerical example and a case study of the Tennessee Eastman (TE) process are used to verify the proposed model, PCA and KPCA fault detection methods are chosen to comparative study, the results show that the proposed method is feasible and efficient; and evidently improves the effect of fault detection.
Control charts as an effective tool of the statistical process control, the patterns recognition plays an important role in dealing with the complex production process quality problems. For the identification difficulty of control chart mixture pattern, statistical and sharp features of observation data are used as the eigenvalue of quality information, and then principal component analysis as the second feature extraction to get the effective data for the classifier. Multi-class support vector machines apply for recognizing the control chart patterns. And adaptive mutation particle swam optimization is used to optimize the SVM classifier by searching the best values of the parameters of SVM. The six basic patterns and four mixed patterns are used to analyze the proposed model, and the other three methods for comparative study, respectively, neural networks, support vector machine and support vector machines based on principal component analysis. The simulation results show the proposed method has better recognition accuracy compared with other methods. It provides reference value for the control chart pattern recognition research.
According to the complex production process characteristics of multi-input, multi-output, nonlinear, a multi-model quality prediction approach based on local models is proposed. Firstly, classify the operation condition using the K-means clustering algorithm; then establish the local quality prediction models using support vector machine based on the multiple loading conditions; finally, get the local model weights using the adaptive mutation particle swam optimization, so that obtain the global model to realize the production process quality prediction. A complex processe, the normal mode of the TE process, is used to simulate and verify the proposed multi-model, and then uses local model, BP and SVM model as the comparative study. The results show that the prediction of the model evidently improves the prediction accuracy, which compared to other forecasting methods, and the relative error is controlled about1%. At the same time, the results also show that the proposed method is feasible and efficient.
The complexity production process is difficult to describe by a purely abstract mathematical model, and manufacturers hope to simulate the production process by virtual simulation, which can analyze the quality factors of the production process and find the factors of its quality problems. Hot mix asphalt (HMA) aggregate gradation control, which based on the frame work of mathematical model, simulates HMA mix aggregates gradation under different production utilizing discrete event simulation software Arena. Multi-factors control logic is established by the embedded compiler module of Arena software, and then image output is used to observe the production status changes. The simulations results show that system optimize control results are related with control strategies, the suitable control strategy can make a significant reduction in the quality problems. Simulation control method provides a good way for HMA quality control. It greatly shortens the test time, timely controls gradation deviation, and improves the overall quality of the product. It can be better to reduce the deviation for improving the production quality and provides an efficient way for HMA gradation control in the real world.
In this paper, the intelligent methods, like support vector machines, particle swam optimization, kernel principal component analysis, wavelet packet analysis and simulation technology, are used for quality control of complex production process, Control chart pattern recognition, fault detection, quality prediction and virtual intelligent control are adopted to implement quality control of complex production process. The proposed methods have high theoretical value and production practical significance, and laid a solid foundation for the future study.
支持向量机是建立在统计学习理论的VC维理论和最小结构风险基础上的一种机器学习智能方法。通过核函数实现非线性问题向线性问题的转化,能较好的解决小样本、非线性、高维数、局部最小等问题,具有很强的泛化能力。粒子群算法模拟鸟群捕食行为,其算法概念简单,控制参数少,易于实现,同时兼有进化计算和群智能优化的特点,通过个体间的协作与竞争,实现对复杂空间最优解的搜索。粒子群算法是解决整数非线性优化问题、非线性连续优化问题和组合优化问题等方面的有效优化工具。本文利用支持向量机作为建立复杂生产过程质量模型的工具,将粒子群算法及其改进算法应用于支持向量机参数优化、模型最优解选择等问题中,从而实现复杂生产过程的质量控制。
针对复杂生产过程故障检测问题,本文将小波包分析方法作为过程样本数据的消噪工具,通过多层次划分逼近原信号的方法消除样本数据中的噪音和干扰,提取高效的数据样本,建立基于核主元分析的多变量统计过程监控模型,实现非线性问题向线性问题的转化,从而进行复杂生产过程的故障检测。本文通过1个数值算例和TE复杂化工过程故障分析进行故障检测研究,并与PCA和KPCA故障检测方法进行对比研究,证明所提出方法的可行性和有效性。
控制图作为统计过程控制的有效工具,控制图模式的识别在处理复杂生产过程质量问题中发挥着重要的作用。针对目前识别较难的控制图混合模式,本文首先提取样本数据的统计特征和形状特征值作为质量信息特征值,再利用主元分析方法进行第二次特征提取,得到高效的样本数据信息。利用支持向量机泛化能力强、识别精度高的特点实现控制图模式的多分类识别,同时采取自适应变异粒子群算法进行多分类支持向量机的参数优化。本文提出利用六种控制图基本模式和四种控制图混合模式对所提出的模型进行分析,通过与神经网络、支持向量机和主元分析-支持向量机这三种方法的对比试验,结果表明本文所建立的控制图模式识别方法能取得更高的分类识别率,为控制图模式识别研究提供一定的参考价值。
针对复杂生产过程的多输入、多输出、非线性的特点,提出了一种基于局部模型的多工况过程质量预测方法。首先利用K均值聚类方法进行复杂数据处理,实现复杂生产过程工况的划分,利用所需样本小、学习能力强的支持向量机回归原理建立各工况下的局部模型,再利用自适应粒子群算法求得各局部模型最优加权值,建立预测全局多模型,从而实现复杂生产过程的质量预测。通过TE过程的正常模式这一复杂过程对所建立的多模型预测方法进行仿真预测,并与局部模型、BP神经网络模型和支持向量机模型进行对比研究,结果表明本模型的预测效果相对其他预测方法较好,相对误差控制在1%左右,同时表明其预测方法的可行性和有效性。
复杂生产过程很难用单纯抽象的数学模型进行描述,生产商希望通过虚拟仿真的方式动态模拟生产过程,对生产过程的各质量因素进行分析,得到影响其质量问题的因素。本文在沥青混合料生产过程的级配控制的数学模型框架下,利用Arena仿真软件建立人机交互式界面,根据实际生产情况进行参数设定,模拟特定情况下的沥青混合料生产情况。并通过Arena软件内嵌的编译模块,编写不同的控制方法,实现沥青混合料质量控制的多因素控制逻辑,并利用图像输出实时观察生产状态的变化。模型试验结果表明系统优化控制与控制策略相关,适合的控制策略能使系统大幅度降低质量问题,仿真控制方法为沥青混合料级配的质量控制提供了很好的途径,极大地缩短产品的试验时间,及时控制级配偏差,有利于提高产品的整体质量,表明虚拟过程质量控制方法是复杂生产过程质量控制的一种有效途径。
本文利用支持向量机、粒子群算法、核主元分析、小波包分析等智能方法以及仿真技术进行复杂生产过程质量控制研究,主要从故障检测、控制图模式识别、质量预测和虚拟控制四个方面实现复杂生产过程质量的智能控制。文中所提的方法具有较高的理论价值和生产实践意义,并为后续的研究奠定了坚实的基础。
Quality, as one of the essential factors of the socio-economic development, has been got more and more people's attention. With the development of science and technology, the quality management of increasingly complex production processes has become a major subject of the researchers. Quality management implements all activities of the products or services of quality planning, quality control, quality assurance and quality improvement, which are based on the formulation of quality policy. Process quality control, as a key part of the quality control, is the main stage of the product creation and an effective way to achieve quality management to ensure process quality. With increasingly complex production process, the process has the characteristics of high-dimensional and non-linear data, the uncertainty process model, sub-process interfere with each other and strong coupling, its quality control is more difficult than the traditional production process. In this paper, the complex system theory combined with the diversified, intelligent quality control methods are used to realize the quality control of the complexity production process.
Support vector machine (SVM) is an intelligent method of machine learning based on VC-dimensional theory of statistical learning theory and structural risk minimization. SVM uses the kernel function to make the nonlinear problem transform to linear problem, which makes it better to solve the small samples, nonlinearity, high dimension and local minima problems, and has greater generalization ability. Particle swarm optimization (PSO) algorithm simulates birds'predatory behavior to search the optimal solution of complex space by the collaboration and competition between individuals, which has the characteristics of simple concept, few control parameters, easy to implement, evolutionary computation and swarm intelligence optimization. PSO is an effective optimization tool to solve integer nonlinear optimization, nonlinear continuous optimization problems and combinatorial optimization problems. In this paper, support vector machine as a tool to create complex production process quality model, the particle swarm algorithm and its improved algorithm are adopted to optimize the parameters of support vector machine and select model optimal solution, so that implement the quality control of complex production process.
The fault detection of complex production process is a significant problem of quality control. In this paper, wavelet packet transform is chosen as the denoising tools of the process sample data, which uses a multi-hierarchical division method to eliminate the noise and interference of sample data to get the approximation of the original signal, so that extracts the effective data samples. And then, multivariate statistical process monitoring model, which use kernel principal component analysis to make the nonlinear problem transform to linear problem, is proposed to realize the fault detection of complex process. A numerical example and a case study of the Tennessee Eastman (TE) process are used to verify the proposed model, PCA and KPCA fault detection methods are chosen to comparative study, the results show that the proposed method is feasible and efficient; and evidently improves the effect of fault detection.
Control charts as an effective tool of the statistical process control, the patterns recognition plays an important role in dealing with the complex production process quality problems. For the identification difficulty of control chart mixture pattern, statistical and sharp features of observation data are used as the eigenvalue of quality information, and then principal component analysis as the second feature extraction to get the effective data for the classifier. Multi-class support vector machines apply for recognizing the control chart patterns. And adaptive mutation particle swam optimization is used to optimize the SVM classifier by searching the best values of the parameters of SVM. The six basic patterns and four mixed patterns are used to analyze the proposed model, and the other three methods for comparative study, respectively, neural networks, support vector machine and support vector machines based on principal component analysis. The simulation results show the proposed method has better recognition accuracy compared with other methods. It provides reference value for the control chart pattern recognition research.
According to the complex production process characteristics of multi-input, multi-output, nonlinear, a multi-model quality prediction approach based on local models is proposed. Firstly, classify the operation condition using the K-means clustering algorithm; then establish the local quality prediction models using support vector machine based on the multiple loading conditions; finally, get the local model weights using the adaptive mutation particle swam optimization, so that obtain the global model to realize the production process quality prediction. A complex processe, the normal mode of the TE process, is used to simulate and verify the proposed multi-model, and then uses local model, BP and SVM model as the comparative study. The results show that the prediction of the model evidently improves the prediction accuracy, which compared to other forecasting methods, and the relative error is controlled about1%. At the same time, the results also show that the proposed method is feasible and efficient.
The complexity production process is difficult to describe by a purely abstract mathematical model, and manufacturers hope to simulate the production process by virtual simulation, which can analyze the quality factors of the production process and find the factors of its quality problems. Hot mix asphalt (HMA) aggregate gradation control, which based on the frame work of mathematical model, simulates HMA mix aggregates gradation under different production utilizing discrete event simulation software Arena. Multi-factors control logic is established by the embedded compiler module of Arena software, and then image output is used to observe the production status changes. The simulations results show that system optimize control results are related with control strategies, the suitable control strategy can make a significant reduction in the quality problems. Simulation control method provides a good way for HMA quality control. It greatly shortens the test time, timely controls gradation deviation, and improves the overall quality of the product. It can be better to reduce the deviation for improving the production quality and provides an efficient way for HMA gradation control in the real world.
In this paper, the intelligent methods, like support vector machines, particle swam optimization, kernel principal component analysis, wavelet packet analysis and simulation technology, are used for quality control of complex production process, Control chart pattern recognition, fault detection, quality prediction and virtual intelligent control are adopted to implement quality control of complex production process. The proposed methods have high theoretical value and production practical significance, and laid a solid foundation for the future study.
引文
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