数据驱动下的半导体生产线动态调度方法

本文研究了一种数据驱动下的半导体生产线调度框架,该框架基于调度优化数据样本,应用机器学习算法,获得动态调度模型,通过该模型,对于半导体生产线,能够根据其当前的生产状态,实时地定出近似最优的调度策略.在此基础上,利用特征选择和分类算法,提出一种生成动态调度模型的方法,并且具体实现出一种混合式特征选择和分类算法的调度模型:先采用过滤式特征选择方法对生产属性进行初步筛选,然后再采用封装式特征选择和分类方法生成模型以提高模型生成的效率.最后,在某实际半导体生产线上,对在所提出的框架上采用6种不同算法实现的动态调度模型进行测试,并对算法性能数据和生产线性能据进行对比和分析.结果表明,数据驱动下的动态调度方法优于单一的调度规则,同时也能满足生产线调度实时性要求.在数据样本较多的情况下,建议采用本文所提出的方法.     

数据驱动的集成化工过程系统

提出数据驱动的集成化工过程系统（ＩＣＰＳ）的功能和总体结构．开发了适合化工过程模拟的数据库管理子系统，开发了面向数据库库化工单元模块和过程模块，按面向对象的策略在微机上实现了集成化工过程系统．     

基于模型驱动的数据映射技术研究

给出了数据词典的逻辑结构,以数据词典的方式在元模型层上对数据模型进行统一描述,在数据词典的基础上分析了异构数据模型之间的数据映射关系。采用EBNF范式对描述映射关系的语法进行形式化定义和描述,给出映射关系的相关语义说明,为映射关系可视化建模提供图形符号元素的表示方法,使异构数据间的集成以模型驱动的方式实现。     

实验数据驱动虚拟模型的方法研究

该文提出了如何运用实验数据驱动虚拟模型进行运动仿真的新方法。该方法利用ADAMS软件在虚拟环境中构建一个已存在实体的虚拟模型 ,然后利用“导入数据的方法”将反映实体运动的实验数据加载于虚拟模型 ,从而驱动该虚拟模型按照真实物体那样运动。该文以一个受到外力和自身重力作用的小球为例 ,通过将获得的小球运动的实验数据导入的方法 ,成功驱动了小球实体的虚拟模型 ,这样就可以在虚拟环境中观察到小球的运动规律 ,从而验证了该文所提方法的正确性和可行性     

数据驱动的故障预测方法研究

随着设备复杂性和使用环境苛刻度的不断增加,关键部件的可靠性和稳定性要求与日俱增。为实现关键部件的全面监控和故障前有效干预,故障预测技术应运而生,基于数据驱动的故障预测方法具备的适用范围广、预测精度高和建模较易等优势使其成为近年来研究的热点。论述了故障预测技术的相关理论和内涵。对当前的主流——基于数据的故障预测技术进行详细介绍,并概述了国内外的最新研究成果。最后,探讨了故障预测领域亟待解决的问题和未来的发展方向。     

数据驱动的DFD及其编译

本文基于数据驱动机制，提出了一种描述数据流程图ＤＦＤ的中间语言并实现了对它的编译，提出了关系矩阵，行列向量表示法等，采用双缓冲，递归下降子程序等方法实现了各种必需的编译分析及目标代码的生成，是一种有效的辅助ＭＩＳ开发的工具。     

网络数据驱动模型

本文在传统的客户机与服务器之间的软件体系基础上,提出了网络数据驱动模型这一新的结构。     

网络数据驱动模型

本文在传统的客户机与服务器之间的软件体系基础上，提出了网络数据驱动模型这一新的结构。     

数据驱动的浮选过程运行反馈解耦控制方法

浮选过程是利用矿物本身的亲水或疏气性质或经药剂处理得到的亲水或疏气性质进行矿物分离的物理过程.本文通过建立以矿浆液位和矿浆流量为输入,以浮选过程的精矿品位与尾矿品位为输出的多变量、强耦合、非线性、时变的运行过程模型,利用未建模动态前一拍可测的特点,提出了包括矿物品位运行过程控制器驱动模型、PID控制器、反馈解耦控制器、未建模动态补偿器的数据驱动的一步最优未建模动态补偿PID解耦控制方法,实现了消除稳态误差、静态解耦与未建模动态的补偿,通过浮选过程运行反馈控制仿真实验验证了本文所提方法的有效性.     

Data-driven soft sensor development based on deep learning technique

In industrial process control, some product qualities and key variables are always difficult to measure online due to technical or economic limitations. As an effective solution, data-driven soft sensors provide stable and reliable online estimation of these variables based on historical measurements of easy-to-measure process variables. Deep learning, as a novel training strategy for deep neural networks, has recently become a popular data-driven approach in the area of machine learning. In the present study, the deep learning technique is employed to build soft sensors and applied to an industrial case to estimate the heavy diesel 95% cut point of a crude distillation unit (CDU). The comparison of modeling results demonstrates that the deep learning technique is especially suitable for soft sensor modeling because of the following advantages over traditional methods. First, with a complex multi-layer structure, the deep neural network is able to contain richer information and yield improved representation ability compared with traditional data-driven models. Second, deep neural networks are established as latent variable models that help to describe highly correlated process variables. Third, the deep learning is semi-supervised so that all available process data can be utilized. Fourth, the deep learning technique is particularly efficient dealing with massive data in practice.     

基于数据的动态舞台机械运动设计与仿真

针对现代演艺节目的特点,结合多媒体动态舞台的制作,提出了在舞台机械运动的设计和控制方面的处理思路,即通过抽象的数据处理双向连接形象的舞台设计与舞台表达。以数据为核心,利用数据建模的方法进行三维仿真舞台的设计,采用舞台机械运动关键帧技术进行仿真舞台数据提取和处理。针对机械的性能指标和物理特点,采用与抛物线拟合的线性函数进行舞台运动的时间误差的控制。将舞台机械的运行数据进行整合和处理,通过三维舞台动画制作方法进行仿真呈现,形成设计与实施的双向数据流。利用双向数据流产生的数据,不仅为舞台的多媒体制作,舞台安全检测,舞台前视觉仿真等现代多媒体动态舞台模块提供统一准确的数据基准,而且改善了其制作流程,缩短了制作周期。     

基于数据驱动的铜闪速熔炼过程操作模式优化及应用

针对铜闪速熔炼过程工艺指标无法在线检测、过程建模及优化控制困难的问题, 研究了基于数据驱动的操作模式优化方法. 论文在铜闪速熔炼过程特点分析的基础上, 定义了基于数据驱动的操作模式优化的基本概念, 提出了基于数据驱动的操作模式优化控制框架, 研究了基于数据的冰铜温度、冰铜品位、渣中铁硅比的工艺指标预测模型、炉况的综合评价模型及闪速熔炼过程的操作模式优化. 基于大量工业运行数据和炉况评价模型构建优化操作模式库, 提出了将模糊C均值聚类与混沌伪并行遗传算法相结合的匹配算法, 从优化操作模式库中寻找与当前工况相匹配的最优操作模式, 从而实现熔炼过程的优化控制. 在铜闪速熔炼生产中的实际应用证明了该方法的有效性.     

Data-driven predictive control for solid oxide fuel cells

This paper is concerned with predictive control of solid oxide fuel cells (SOFC) based on a benchmark model commonly studied in the dynamic SOFC modeling/control literature. It has been shown in previous studies that control of SOFC is challenging owing to the slow response and tight operating constraints. In this paper, we apply a data-driven predictive control approach to solving the control problem of the SOFC system. The predictive control applied is completely data based. In addition, unlike other data-driven predictive control designs, the proposed approach can deal with systems without complete on-line measurement of all output variables. Simulation results have demonstrated the feasibility of the control application.     

基于数据驱动的故障诊断技术研究现状及展望

对基于数据驱动的过程故障诊断方法进行了总结和划分,其中包含多元统计方法、机器学习方法、流形学习方法等。将各类基于数据驱动的故障诊断方法的原理、研究进展及其在工业过程中的应用进行了描述和分析,最后指出这一领域中需要进一步解决的问题以及近期的研究热点。     

Data-driven diagnosis of sensor precision degradation in the presence of control

This paper considers the precision degradation type of sensor faults within control loops. In a closed loop, sensor faults propagate through controller to manipulated variables and disturb the other process variables, which obscures the source of sensor faults but receives less attention in existing methods of data-driven sensor fault diagnosis. With the assumption that only closed-loop data in normal condition are available, difficulty arises due to the facts that little a priori knowledge is known about closed-loop sensor fault propagation and the open-loop process model may not be identifiable. The proposed method in this paper constructs residual that is regarded as including two parts: the first part is the current sensor faults whose fault direction is known to be the identity matrix; and for the purpose of diagnosing the first part, the second part is considered as the disturbance which is affected by noises and past sensor faults due to unknown fault propagation. The disturbance variance is minimized in residual generator design to improve fault sensitivity. And the corresponding disturbance covariance is estimated and then utilized in residual evaluation. The proposed method in this paper is motivated by a pioneer work on closed-loop sensor fault diagnosis which performs principal component analysis in the feedback-invariant subspace of the closed-loop process outputs. But it is revealed by the proposed method that the feedback-invariant signal is affected by past sensor faults, leading to performance degradation of the pioneer work. The improvement of the proposed approach is due to analysis of residual dynamics and explicit handling of the disturbance in residual evaluation, which is not considered in the pioneer work. A simulated 4 × 4 dynamic process and a simulated two-product distillation column are studied to verify the effectiveness of the proposed approach compared to the existing principal component analysis method in feedback-invariant subspace.     

Data-driven monitoring and validation of experiments on automotive engine test beds

Engine test beds are widely used to estimate automotive engine parameters and design controllers in the preliminary development phase. The controller parameters are optimized to fulfill emission, fuel consumption and driving comfort requirements and they will be further validated on chassis dynamometer and road driving experiments. It is common that the results of two experiments deviate, due to some external disturbances or faults. The main purpose of this paper is to demonstrate the application of data-driven fault diagnosis techniques to detect the deviations in the experiments and analyze their root-causes to reduce the costs and time of the engine design and its control concept. To this end, two different methods are introduced for detection of the problems in the experiment. Based on the results of the detection step, a fault isolation technique has been proposed to support test engineers in finding the cause of the deviations. The results have been demonstrated on an industrial engine test bed and the effectiveness of the methods is discussed.