基于OPC UA的船舶数据监控系统的设计与实现

设计一种基于OPC UA的船舶数据监控系统,采用OPC UA协议解决船底层不同硬件接口和数据传输协议多样所带来的数据采集和传输的通用性问题,有效地监控设备实时工作状况。数据采集端将数据转换为统一的OPC UA格式,OPC UA客户端通过统一的数据传输接口获取采集端的数据并把数据存储到数据库中。通过Django、JavaScript等相关Web技术,建立B/S模式下的数据可视化平台,以图表形式显示实时数据,使船员及时掌握设备运行状态并进行有效决策,促进船舶领域向着信息化、智能化的方向发展。     

基于数字孪生技术的三维智能化系统设计

为了提升三维智能化水平,设计基于数字孪生技术的三维智能化系统。通过现场层管理数字孪生体映射的物理实体与监测对象,为三维智能化系统提供需要的基础数据;感知层利用温度传感器与颜色传感器等,感知现场层管理的物理实体相关数据;数据存储层利用分布式文件与关系型数据库,存储感知的数据;功能层利用数据预处理模块缺失值处理存储的数据;层次化建模模块,依据缺失值处理后的数据,建立物理实体的几何模型与属性模型,融合几何模型与属性模型,得到孪生体模型;孪生体模型驱动单元,利用三维模型驱动方法,驱动孪生体模型,实现物理实体与孪生体的同步映射,得到孪生数据;仿真与预测模块依据孪生数据,可视化监测与分析物理实体的工作状态;通过应用层为用户实时呈现物理实体的动作仿真与实时监控结果。实验证明：该系统可有效感知物理实体的相关数据,建立孪生体模型;该系统可有效实现物理实体的可视化管理,提升三维智能化水平。     

基于OPC UA的电厂水务数据采集系统设计开发

在传统的工业制造领域,现场设备的基于开放通信标准的无缝集成是进行工厂数据采集和统一监控的必要条件。作为一个开放的协议,基于OPC UA的数据采集系统可以解决现场工业设备通讯协议的多样性带来系统软件开发的复杂度问题。以电厂水务系统中存在的各类感知层设备为对象,通过建立基于OPC UA的数据通信规范,将智能传感器、智能控制器和扩展通信接口后的普通IO设备连接到OPC UA服务器,实现现场数据的采集;再通过建立合适的客户端系统,实现对采集数据的数据库存储和在线访问。运行结果表明该系统的设计符合电厂水务系统的实际需求,为系统提供了精确及时的基础数据,提升了系统的管理效率。     

基于OPC UA的防水卷材生产线远程监控系统的研究

为解决防水卷材生产设备数据采集和管理存在的难题,本文开发了一种基于OPC UA的远程监控系统,并以OPC UA规范为核心,以防水卷材设备为对象,实现了防水卷材生产线的互联互通与远程管理。所开发系统可将防水材料设备运行数据采集到总服务器进行存储,并可通过手机APP实时查询。管理人员不再受地域和时间限制,可随时查看车间生产状况,分析生产数据,使企业的生产管理更加安全有效。     

基于数字孪生的变电设备运维系统及其构建

针对变电设备周期状态管控难、运检效率低等问题,基于数字孪生理论,构建基于真实变电设备运维的数字化模型及系统。首先,在信息层建立能反映变电设备换流变、调相机、GIS（Gas Insulated Substation）这3类设备真实状态的数字孪生体;其次,基于换流变、调相机、GIS历史大数据,通过数字孪生体的变电站设备进行统计分析,并根据采集的实时数据、运维数据来预测变电站设备下一时刻的状态,使变电设备实现实际变电站内物理层与信息层数据的融合;最后,以变电站设备运维为对象,采用信息物理融合系统进行运维数据的集成和同步,形成最终变电设备运维数字孪生框架系统。相关研究表明,运用数字孪生技术可以对变电设备运维系统运行效率的提升和对变电站整体智能化提供强有力的技术支撑。     

基于用于过程控制的对象链接和嵌入统一架构发布订阅的设备横向实时通信实验研究

随着开放式自动化系统的发展,设备间水平实时通信的需求日益增加。OPC UA是一套平台独立面向服务且用于工业通讯的数据交互规范,并提出了发布订阅(PuB/Sub)通信模型,该通信模型可以映射到实时通信网络中。时间敏感网络(TSN)协议为标准以太网提供实时传输能力和兼容其他标准以太网。OPC UA PuB/Sub通信模型与TSN融合,可以满足机器和设备间横向实时通信的需求。搭建了一个基于OPC UA PuB/Sub over TSN的通信实验台,并通过用两台工控机实现电机同步控制来测试基于OPC UA PuB/Sub的设备间的横向通信实时性能,实验结果说明TSN网络能够为基于OPC UA Pubsub的设备间横向通信提供实时性保障。     

断路器柔性装配车间数字孪生系统设计

为优化断路器装配车间的产线结构和作业方法,结合数字孪生技术,提出一种基于多机器人运动控制的断路器柔性自动化车间装配方案。面向自动化装配单元,结合工业机器人的柔性装配工艺及方法,对实体装配车间进行全物理属性的数字化建模,同时建立多机器人的运动学控制模型,将机器人的三维运动模型应用于虚拟孪生场景。通过数据的交互传递,实现物理单元与虚拟单元的实时链接,将车间机器人的运动轨迹、装配状态、作业运送流程等数据信息进行实时显示,从而实现断路器柔性装配数字孪生系统的搭建与同步映射。实验结果证明,所提方案对实现断路器柔性装配有显著效果。     

OPC UA技术在冶金设备监测系统中的应用

OPC UA技术在工业控制方面有着不可替代的优势,主要体现在其跨平台性和实时性上,可以方便地解决工业现场监控过程中各个子系统和底层设备之间互操作以及互通信的难题。结合冶金现场设备监测系统的搭建,对OPC UA技术规范进行了研究,开发了OPC UA客户端和服务器,实现了对冶金设备运行状态的实时监测并及时制定故障应对方案的目的。 OPC UA技术在信息建模与跨平台方面的强大优势预示着基于OPC UA技术的集成系统必将成为今后的开发热点。     

实时在线监控系统的三维可视化方案

为解决核反应堆辐射及不可接近环境下工人运维的技术难题,融合新一代信息技术,将数字孪生技术与工业互联技术相结合,提出了一套三维数据可视化实现方案,解决了数字孪生应用中三维模型呈现、实时数据对接、模型算法软件对接,以及数据可视化等关键问题。首先,利用3D Studio Max、SolidWorks等建模软件构建三维模型,并结合前端设计、虚拟场景渲染等技术,实现数字孪生可视化场景构建;其次,基于Node. js运行环境搭建WebSocket服务器,并通过Node-EPICS事件驱动读取实验物理和工业控制系统（EPICS）的过程变量,实现对设备支持层数据更新事件的监听;最后,利用Socket. io套接字创建双向数据通道实现服务器到Unity客户端的实时数据传输。使用该方案构建的反应堆三维监控系统现已应用于中国科学院（CAS）钍基熔盐堆（TMSR）核能项目,该系统实现了核反应设备结构及物理特性在虚拟环境中的数字映射,具备网络通信、数据显示等功能,且数据更新周期达到100 ms,有助于监控人员掌握现场实际情况与指导运维检修。     

基于数字孪生的雕刻机人机交互系统设计

针对雕刻机应用过程中的实时监测困难,数据呈现单一,成本较高等问题,设计了一套基于数字孪生的雕刻机人机交互系统;首先使用SolidWorks软件设计与实际雕刻机高度匹配的三维孪生模型,随后根据实际系统的运动学模型建立Simulink仿真并编写控制算法控制三维孪生模型的运动,完成虚拟调试功能,最后将三维孪生模型与雕刻机结合,使模型根据实际雕刻机运行得到的实时数据完成设备状态监测功能;实验结果表明,文章所设计的雕刻机人机交互系统具有较高的实时性和精确度,能够保证良好的交互性与虚实结合性,有效提高监控效率,降低故障发生率,在工业现场具有广泛的应用价值.     

Hybrid learning-based digital twin for manufacturing process: Modeling framework and implementation

Digital twin (DT) and artificial intelligence (AI) technologies are powerful enablers for Industry 4.0 toward sustainable resilient manufacturing. Digital twins of machine tools and machining processes combine advanced digital techniques and production domain knowledge, facilitate the enhancement of agility, traceability, and resilience of production systems, and help machine tool builders achieve a paradigm shift from one-time products provision to on-going service delivery. However, the adaptability and accuracy of digital twins at the shopfloor level are restricted by heterogeneous data sources, modeling precision as well as uncertainties from dynamical industrial environments. This article proposes a novel modeling framework to address these inadequacies by in-depth integrating AI techniques and machine tool expertise using aggregated data along the product development process. A data processing procedure is constructed to contextualize metadata sources from the design, planning, manufacturing, and quality stages and link them into a digital thread. On this consistent data basis, a modeling pipeline is presented to incorporate production and machine tool prior knowledge into AI development pipeline, while considering the multi-fidelity nature of data sources in dynamic industrial circumstances. In terms of implementation, we first introduce our existing work for building digital twins of machine tool and manufacturing process. Within this infrastructure, we developed a hybrid learning-based digital twin for manufacturing process following proposed modeling framework and tested it in an external industrial project exemplarily for real-time workpiece quality monitoring. The result indicates that the proposed hybrid learning-based digital twin enables learning uncertainties of the interaction of machine tools and machining processes in real industrial environments, thus allows estimating and enhancing the modeling reliability, depending on the data quality and accessibility. Prospectively, it also contributes to the reparametrization of model parameters and to the adaptive process control.     

The rapid construction method of the digital twin polymorphic model for discrete manufacturing workshop

The modeling process is resource-intensive, time-consuming, and expensive due to the large number and variety of production and auxiliary equipment in discrete manufacturing plants. The popular twin-model construction method based on a single type of equipment cannot meet the demand for rapid construction and high-quality model mapping in large discrete manufacturing plants. This paper proposes a strategy for developing a digital twin polymorphic model (DTPM) for discrete manufacturing workshops to meet complex manufacturing business scenarios, improve the richness of digital twin model varieties, construction efficiency and intelligence, and form an efficient equipment model construction method. The complete element information and real-time dynamic data of the minimal component unit (Functional Components, FC) of DTPM are clustered and analyzed. In addition, properties of the functional component information model are characterized from several dimensions, such as geometry, physics, and behavior. Furthermore, the FC with highly concentrated primary components is established based on the object-oriented derivation inheritance and attribute reuse hybrid drive technique. FC drastically reduces the duration of the digital twin model development process through encapsulation, inheritance, polymorphism, and other technologies. On this premise, the DTPM construction method that deeply decouples the physical structure and functional methods is proposed. DTPM integrates adaptive information interaction technology to accomplish intelligent data connection, dynamic data updating, and digitally accurate mapping of static-dynamic-virtual multidimensional models in discrete production workshops. Lastly, the method's validity is confirmed by creating a large discrete production workplace. The results indicate that the proposed technique may significantly enhance the model variety and building efficiency of large-scale digital twin workshop systems.     

A decision-making framework for dynamic scheduling of cyber-physical production systems based on digital twins

Nowadays, one important challenge in cyber-physical production systems is updating dynamic production schedules through an automated decision-making performed while the production is running. The condition of the manufacturing equipment may in fact lead to schedule unfeasibility or inefficiency, thus requiring responsiveness to preserve productivity and reduce the operational costs. In order to address current limitations of traditional scheduling methods, this work proposes a new framework that exploits the aggregation of several digital twins, representing different physical assets and their autonomous decision-making, together with a global digital twin, in order to perform production scheduling optimization when it is needed. The decision-making process is supported on a fuzzy inference system using the state or conditions of different assets and the production rate of the whole system. The condition of the assets is predicted by the condition-based monitoring modules in the local digital twins of the workstations, whereas the production rate is evaluated and assured by the global digital twin of the shop floor. This paper presents a framework for decentralized and integrated decision-making for re-scheduling of a cyber-physical production system, and the validation and proof-of-concept of the proposed method in an Industry 4.0 pilot line of assembly process. The experimental results demonstrate that the proposed framework is capable to detect changes in the manufacturing process and to make appropriate decisions for re-scheduling the process.     

Digital twin-driven intelligent production line for automotive MEMS pressure sensors

The equipment and technological processes used in manufacturing electronic products are gradually being automated and networked. Currently, digital twin technology continues to evolve and mature. The electronics manufacturing industry is undergoing an intelligent and digital transformation. Micro-electro-mechanical system (MEMS) sensors have been widely used in the automotive field due to their small size, low cost, and high reliability. In this study, a new intelligent production line for automotive MEMS pressure sensors driven by digital twin is individually designed. The intelligent production line system consists of physical production lines, digital production lines, twin data, and data service systems. The technology of multi-source heterogeneous data acquisition is used to process and analyze data collected in real time in a physical production line. Based on the technology of parallel control, the physical and digital production lines are synchronized. To obtain optimal process parameters, a process database is established through the analysis of the key processes of the production line. Three types of automotive MEMS pressure sensors are successfully manufactured in the constructed digital twin-driven intelligent production line. The intelligent production line can realize 24-h unattended operation. The product yield is above 98 %, and the takt time is less than 16 s.     

基于一致性度量的数字孪生模型实时自修正

数字孪生技术解决了信息物理世界的融合难题,在工业互联网领域里获得了十分广泛的应用。为解决数字孪生与物理实体的动态修正问题,本文提出一种基于一致性度量的数字孪生模型实时自修正方法。利用数据变化快慢将模型分为渐变模型和快速模型2个部分,构建参数快速搜索方法,结合拉丁超立方全局搜索和贪婪局部搜索,并引入迭代更新机制,实现物理实体和数字孪生体的一致性度量。实验结果表明,数字孪生模型通过优化模型可调参数的选取过程,改善可调参数选取随机性的问题,实现模型与物理实体高度一致性,达到了模型实时自修正要求。     

基于数字孪生的智能车间系统仿真加速测试方法

为解决当前制造系统软件可靠性仿真测试时间长、测试环境难以搭建等问题,提出采用数字孪生技术与智能车间系统仿真加速测试相结合的方法;建立智能车间高保真数字孪生模型替代现实生产车间系统用于制造系统软件的可靠性仿真测试,首先要构建包含产品、设备资源、工艺流程等系统级仿真模型;同时,为仿真车间生产事件流程,在模型中,还需结合生产实际情况,设置设备间通信协议、通信数据以及生产线事件及队列顺序,真实模拟系统运行环境;通过构建步进电机产线数字孪生模型,仿真加工装配流程,运行智能车间系统软件,采用仿真时钟推进机制开展加速测试,验证了该方法的有效性和实用性,对开展工业系统软件高保真快速测试评估具有一定的借鉴意义。     

企业离散式智能设备预测性维护综述

针对智能设备的大量使用且缺乏根据监测大数据进行故障自动分析、判断与处理的问题,研究了基于物联网技术、大数据技术、边云协同技术的智能设备预测性维护框架和模式.提出针对非智能设备安装传感器实现设备智能化的方法.指出边缘计算负责设备工况数据的实时采集、分析,可快速甄别设备故障并实时报警;云计算聚焦同类设备运行海量历史数据的挖...