Combined strength of holons,agents and function blocks in cyber-physical systems

This paper presents a novel approach to implementing cyber-physical systems (CPS) using the combined strength of holons, agents and function blocks. Within the context, a CPS is represented by a holarchy of multiple holons. Each holon possesses a logical part and a physical part, which mimic the cyber and physical entities of the CPS. During implementation, the two parts of a holon are realised by agents and function blocks for information processing and materials processing, respectively. The objective of this research is to provide a concept map and associate a CPS with holons, agents and function blocks for the ease of system implementation in decentralised or cloud environment.     

Big Data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook

The recent advances in sensor and communication technologies can provide the foundations for linking the physical manufacturing facility and machine world to the cyber world of Internet applications. The coupled manufacturing cyber-physical system is envisioned to handle the actual operations in the physical world while simultaneously monitor them in the cyber world with the help of advanced data processing and simulation models at both the manufacturing process and system operational levels. Moreover, a sensor-packed manufacturing system in which each process or piece of equipment makes available event and status information, coupled with market research for true advanced Big Data analytics, seem to be the right ingredients for event response selection and operation virtualization. As a drawback, the resulting manufacturing cyber-physical system will be vulnerable to the inevitable cyber-attacks, unfortunately, so common for the software and Internet-based systems. This reality makes cybersecurity penetration within the manufacturing domain a need that goes uncontested across researchers and practitioners. This work provides a review of the current status of virtualization and cloud-based services for manufacturing systems and of the use of Big Data analytics for planning and control of manufacturing operations. Building on already developed cloud business solutions, cloud manufacturing is expected to offer improved enterprise manufacturing and business decision support. Based on the current state-of-the-art cloud manufacturing solutions and Big Data applications, this work also proposes a framework for the development of predictive manufacturing cyber-physical systems that include capabilities for attaching to the Internet of Things, and capabilities for complex event processing and Big Data algorithmic analytics.     

On a containerized approach for the dynamic planning and control of a cyber - physical production system

The increased complexity of modern production systems requires sophisticated system control approaches to maintain high levels of flexibility. Furthermore, the request for customized production with the introduction of heterogeneous production resources, increases the diversity of manufacturing systems making their reconfiguration complex and time consuming.In this paper, an end-to-end approach for reconfigurable cyber-physical production systems is discussed, enabled by container technologies. The presented approach enhances flexibility in a cyber-physical production system (CPPS) through the dynamic reconfiguration of the automation system and the production schedule, based on occurring events.High-level management of manufacturing operations is performed on a centralized node while the data processing and execution control is handled at the network edge. Runtime events are generated at the edge and in smart connected devices via means of a variant of IEC61499 function blocks. Software containers manage the deployment and low-level orchestration of FBs at the edge devices. All aspects of the proposed solution have been implemented on a software framework and applied in a small scale CPPS coming from the automotive industry.     

A metamodel for cyber-physical systems

With the advent of the Internet of Things and Industry 4.0 concepts, cyber-physical systems in civil engineering experience an increasing impact on structural health monitoring (SHM) and control applications. Designing, optimizing, and documenting cyber-physical system on a formal basis require platform-independent and technology-independent metamodels. This study, with emphasis on communication in cyber-physical systems, presents a metamodel for describing cyber-physical systems. First, metamodeling concepts commonly used in computing in civil engineering are reviewed and possibilities and limitations of describing communication-related information are discussed. Next, communication-related properties and behavior of distributed cyber-physical systems applied for SHM and control are explained, and system components relevant to communication are specified. Then, the metamodel to formally describe cyber-physical systems is proposed and mapped into the Industry Foundation Classes (IFC), an open international standard for building information modeling (BIM). Finally, the IFC-based approach is verified using software of the official IFC certification program, and it is validated by BIM-based example modeling of a prototype cyber-physical system, which is physically implemented in the laboratory. As a result, cyber-physical systems applied for SHM and control are described and the information is stored, documented, and exchanged on the formal basis of IFC, facilitating design, optimization, and documentation of cyber-physical systems.     

工业控制系统的信息物理因果流模型

工业控制系统向智能控制的发展随着人工智能的因果革命也应该进行因果建模的思考。通过提出工业过程控制系统的单、多层信息物理结构,引入信息、物理因果流,建立过程控制系统的信息物理因果流模型,为依据因果关系设计工业控制系统、分析控制运行机制、实施系统故障预测和监控等提供理论基础和描述框架。     

In-process machine vision monitoring of tool wear for Cyber-Physical Production Systems

The monitoring of tool wear in machining process is becoming a crucial element in the modern production systems to predict the tool lifespan, and consequently the ideal point to replace it, still remains a challenge up to now. On the other hand, Cyber-Physical Systems (CPSs) have attracted researchers in many areas, especially in manufacturing, and they are playing a key role in the integration of heterogeneous software and hardware components. In this paper, an in-process machine vision monitoring of tool wear is integrated into a production system based on the CPS approach. Thereby, a methodology of four phases is proposed, whose goal basically is to raise the requirements, validate the integration, develop the decentralized architecture and finally prove the efficiency, robustness, and capabilities that only cyber-physical systems can bring to a production system. The feasibility and effectiveness of the proposed monitoring system for in-process tool wear is validated in a CNC drilling machining process.     

Manufacturing Data Analysis in Internet of Things/Internet of Data (IoT/IoD) Scenario

ABSTRACT

Computer integrated manufacturing (CIM) has enormous benefits as it increases the rate of production, reduces errors and production waste, and streamlines manufacturing sub-systems. However, there are some new challenges related to CIM operating in the Internet of Things/Internet of Data (IoT/IoD) scenarios associated with Industry 4.0 and cyber-physical systems. The main challenge is to deal with the massive volume of data flowing between various CIM components functioning in virtual settings of IoT. This paper proposes decisional DNA-based knowledge representation framework to manage the storage, analysis, and processing of data, information, and knowledge of a typical CIM. The framework utilizes the concept of virtual engineering object and virtual engineering process for developing knowledge models of various CIM components such as automatic storage and retrieval systems, automatic guided vehicles, robots, and numerically controlled machines. The proposed model is capable of capturing in real time the manufacturing data, information and knowledge at every stage of production, that is, at the object level, the process level, and at the factory level. The significance of this study is that it will support decision-making by reusing the experience, which will not only help in effective real-time data monitoring and processing, but also make CIM system intelligent and ready to function in the virtual Industry 4.0 environment.     

具有随机网络攻击的切换信息物理系统的事件触发控制（英文）

本文研究了随机网络攻击下切换信息物理系统的事件触发控制问题.将信息物理系统描述为一种切换线性系统形式.引入事件触发机制来节省系统资源和减轻网络负载,当误差超过给定阈值时传感器中的采样数据才通过通信网络传输到控制器中.考虑在传感器与控制器的通信网络中受到两种不同特征的随机网络攻击.在网络攻击和所设计的事件触发控制器下,建立了切换随机信息物理系统模型.利用模态依赖平均驻留时间方法构建了相应的切换信号.在设计的事件触发控制器和模态依赖平均驻留时间切换信号下实现了系统的均方指数稳定性,并给出了控制器增益.最后,通过实例验证了所得理论结果的有效性.     

Advanced monitoring platform for industrial wastewater treatment: Multivariable approach using the self-organizing map

Treatment of industrial wastewaters is currently confronting important challenges concerning both cost management of treatment plants and fulfillment of tightening environmental regulations. Online monitoring of wastewater treatment is critical, because changes in the performance of treatment can lead to various problems such as decreased efficiency of purification, decreased energy efficiency, or ineffective use of chemicals. Moreover, changes in the operation of a treatment process can inflict changes that have unforeseen consequences, including an increased amount of harmful effluents, and therefore it is essential for a monitoring system to be able to adapt to various process conditions. It seems, however, that the monitoring systems used currently by the industry are lacking this functionality and are therefore only partially able to meet the needs of modern industry. In addition, there is typically a large amount of measurement data available in the industry, for which advanced data processing and computational tools are needed for monitoring, analysis, and control. For this reason, it would be useful to have a monitoring system which could be able to handle a large amount of measurement data and present the essential information on the state and evolution of the process in a simple, user-friendly and flexible manner. In this paper, we introduce an adaptive multivariable approach based on self-organizing maps (SOM) which can be utilized for advanced monitoring of industrial processes. The system developed can provide a new kind of tool for illustrating the condition and evolution of an industrial wastewater treatment process. The operation of the system is demonstrated using process measurements from an activated sludge treatment plant, which is a part of a pulp and paper plant.     

Intelligent welding system technologies: State-of-the-art review and perspectives

Welding systems are being transformed by the advent of modern information technologies such as the internet of things, big data, artificial intelligence, cloud computing, and intelligent manufacturing. Intelligent welding systems (IWS), making use of these technologies, are drawing attention from academic and industrial communities. Intelligent welding is the use of computers to mimic, strengthen, and/or replace human operators in sensing, learning, decision-making, monitoring and control, etc. This is accomplished by integrating the advantages of humans and physical systems into intelligent cyber systems. While intelligent welding has found pilot applications in industry, a systematic analysis of its components, applications, and future directions will help provide a unified definition of intelligent welding systems. This paper examines fundamental components and techniques necessary to make welding systems intelligent, including sensing and signal processing, feature extraction and selection, modeling, decision-making, and learning. Emerging technologies and their application potential to IWS will also be surveyed, including Industry 4.0, cyber-physical system (CPS), digital twins, etc. Typical applications in IWS will be surveyed, including weld design, task sequencing, robot path planning, robot programming, process monitoring and diagnosis, prediction, process control, quality inspection and assessment, human-robot collaboration, and virtual welding. Finally, conclusions and suggestions for future development will be proposed. This review is intended to provide a reference of the state-of-the-art for those seeking to introduce intelligent welding capabilities as they modernize their traditional welding stations, systems, and factories.     

信息物理融合的智慧能源系统多级对等协同优化

针对能源电力系统的优化管理与控制问题，提出了一种信息物理融合的智慧能源系统（Intelligent energy systems，IES）多级对等协同优化方法.在信息物理融合能源系统（Cyber-physical energy systems，CPES）的基础上，构建了智慧能源系统的局域和广域两级协同优化架构.综合考虑产消者能源实体对等交互过程中的社会福利、供求平衡和需求意愿等因素，基于Stone-Geary函数和双向拍卖机制构建了智慧能源系统能量优化模型，给出了通过收敛判定域引导的全局随机寻优与区域定向寻优策略，有效地提高了算法的局部搜索能力.此外，通过双向拍卖机制的理性定价以及智能合约的辅助服务，有效地实现了用户友好的对等交易模式.仿真实例表明，在社会福利最大化的前提下可获得产消者电力资源最优分配结果，进一步验证了本文方法的有效性和可行性.     

Synchronization of Multi-links Memristor-Based Switching Networks Under Uniform Random Attacks

A variety of influence factors are common to the support networks which are used as cyber-physical systems. In this paper, we consider the problem of finite-time and exponential synchronization for the memristor-based switching networks (MSNs) with multi-links and multiple time-varying delays under uniform random attacks via asymptotic controller and adaptive controller. We propose a more general system model and utilize an analytical method which is different from the classical analytical techniques like set-valued mappings technique and differential inclusions to preprocess the MSNs to a class of switching networks with some uncertain parameters. Then, based on appropriate Lyaponov functionals and linear matrix inequality, several useful criteria ensuring the finite-time synchronization or asymptotic synchronization of MSNs with multi-links and time-varying delays under uniform random attacks via designed control law are obtained. Finally, two numerical examples are designed to show the feasibility and the correctness of our proposed results.     

Data-driven cyber-physical system framework for connected resistance spot welding weldability certification

A cyber-physical system is one of the integral parts of the development endeavor of the smart manufacturing domain and the Industry 4.0 wave. With the advances in data analytics, smart manufacturing is gradually transforming the global manufacturing landscape. In the Resistance Spot Welding (RSW) domain, the focus has been more on the physical systems, compared to the virtual systems. The cyber-physical system facilitates the integrated analysis of the design and manufacturing processes by converging the physical and virtual stages to improve product quality in real-time. However, a cyber-physical system integrated RSW weldability certification is still an unmet need. This research is to realize a real-time data-driven cyber-physical system framework with integrated analytics and parameter optimization capabilities for connected RSW weldability certification. The framework is based on the conceptualization of the layers of the cyber-physical system and can incorporate the design and machine changes. It integrates data from the analytics lifecycle phases, starting from the data collection operation, to the predictive analytics operation, and to the visualization of the design. This integrated framework aims to support decision-makers to understand product design and its manufacturing implications. In addition to data analytics, the proposed framework implements a closed-loop machine parameter optimization considering the target product design. The framework visualizes the target product assembly with predicted response parameters along with displaying the process parameters and material design parameters simultaneously. This layer should help the designers in their decision-making process and the engineers to gain knowledge about the manufacturing processes. A case study on the basis of a real industrial case and data is presented in detail to illustrate the application of the envisioned cyber-physical systems framework.     

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.     

信息能源系统的信-物融合稳定性分析

尽管信息物理系统的稳定性已经得到了广泛的研究,但大部分的学者皆关注于通信网络延时或攻击下的信息物理系统的稳定性问题,无网络通信的信息物理系统的信物融合稳定性分析策略亟待提出.其中,内嵌数字控制系统的并网逆变器系统是一种最简单、最典型的信息能源系统.同时,从效率的角度出发,逆变器的开关/采样频率总是选择尽可能低的频率,其势必产生系统固有延迟时间(控制理论中称为时间延迟).这种延迟时间往往容易引起系统的低频/次同步振荡,弱电网将加剧此现象.为此,提出一种信息能源系统的信-物融合稳定性分析技术.首先,基于柏德近似方法,建立了具有等效延迟时间的信息物理系统阻抗模型.该等效延迟时间由三部分组成,即信息/物理层的采样延迟时间、信息层的计算延迟时间和物理层的脉宽调制延迟时间,其有效地反映了信息-物理相互融合作用的影响.进而设计了稳定禁止区域判据,利用空间映射使开关/采样频率求解过程转化为Hurwitz矩阵辨识问题.在这些空间映射的基础上,最小开关/采样频率通过自适应步长搜索算法获得.最后,仿真和实验结果验证了该方法的有效性.     

Decentralised disturbance-observer-based adaptive tracking in the presence of unmatched nonlinear time-delayed interactions and disturbances

This paper proposes an approximation-based nonlinear disturbance observer (NDO) approach for decentralised adaptive tracking of uncertain interconnected pure-feedback nonlinear systems with unmatched time-delayed nonlinear interactions and external disturbances. Compared with the existing approximation-based NDO approach for uncertain interconnected nonlinear systems where the centralised design framework was proposed, the main contribution of this paper is to develop a decentralised and memoryless NDO-based adaptive control scheme in the presence of unknown time-varying delayed interactions and disturbances unmatched in the control inputs. The recursive design methodology is derived to construct the decentralised NDO and controller where the function approximators used in the decentralised NDO are employed to design the decentralised adaptive controller. From the Lyapunov stability theorem using Lyapunov--Krasovskii functionals, it is shown that all signals of the closed-loop system are semi-globally uniformly ultimately bounded and the tracking errors converge to an adjustable neighbourhood of the origin.     

Edge-cloud orchestration driven industrial smart product-service systems solution design based on CPS and IIoT

The rapid booming of advanced information and communication technologies (ICT) has promoted an encouraging smart, connected product (SCP) market that further triggers the development of manufacturing towards the servitization proposition, viz. smart product-service systems (PSS). Smart PSS aims to provide a solution (product-service) with high satisfaction and less environmental influence by leveraging SCP as the media tool. Its solution design should not just focus on the physical world nor only be enabled by the cloud side, while the cyber world and the edge side must be included in the Industry 4.0. However, only few current researches investigate about the smart PSS, let alone an overall cyber-physical and edge-cloud discussion to support its solution design. In order to fill this gap, this work proposes an edge-cloud orchestration driven solution design based on the cyber-physical systems (CPS) and industrial Internet of Things (IIoT). To make our ideas concrete, a real-life milling process was conducted as an illustrative example. It is hoped that this study can furnish industrial enterprises with meaningful sights in the process of servitization and value co-creation.     

Toward an execution system for self-healing workflows in cyber-physical systems

Cyber-physical systems (CPS) represent a new class of information system that also takes real-world data and effects into account. Software-controlled sensors, actuators and smart objects enable a close coupling of the cyber and physical worlds. Introducing processes into CPS to automate repetitive tasks promises advantages regarding resource utilization and flexibility of control systems for smart spaces. However, process execution systems face new challenges when being adapted for process execution in CPS: the automated processing of sensor events and data, the dynamic invocation of services, the integration of human interaction, and the synchronization of the cyber and physical worlds. Current workflow engines fulfill these requirements only to a certain degree. In this work, we present PROtEUS—an integrated system for process execution in CPS. PROtEUS integrates components for event processing, data routing, dynamic service selection and human interaction on the modeling and execution level. It is the basis for executing self-healing model-based workflows in CPS. We demonstrate the applicability of PROtEUS within two case studies from the Smart Home domain and discuss its feasibility for introducing workflows into cyber-physical systems.     

智能化信息物理系统中非确定性的分类研究

信息物理系统呈现出日趋智能化的特征,而非确定性又是系统中普遍且固有的特性。例如,系统通过传感器感知环境时,会不可避免地存在误差。非确定性若未被妥当处理,往往会影响系统的正确运行,并带来一系列的问题。因此,对信息物理系统中的非确定性进行处理是至关重要的,也是促进信息物理系统进一步智能化的关键。对非确定性进行处理的前提是需要对其有充分的理解和认识,然而现有工作对信息物理系统中非确定性的研究尚处于探索阶段。针对这一问题,研究了信息物理系统中的非确定性分类。具体而言,根据信息物理系统中被广泛认可的5C技术架构对非确定性进行了分类,详细介绍了该架构每一层次上可能存在的非确定性,并结合典型的信息物理系统应用进行了举例说明;同时,总结了当前的相关研究工作,并展望了未来信息物理系统在应对非确定性方面的智能化研究方向。     

Decentralised memory static output feedback control for the nonlinear time-delay similar interconnected systems

In this paper, the problem of decentralised memory static output feedback control for a class of nonlinear time-delayed interconnected systems with similar structure is investigated, where both the linear and nonlinear state vectors involve time delay. The contributions of the paper include the following: (1) a new similar structure is presented via memory static output feedback; (2) by exploiting the structure of interconnected systems, the new integral inequalities, constrained Lyapunov equations and LMI method, the decentralised memory static output derivative feedback controllers with similar structure are designed, which is dependent of time delays, to stabilise the interconnected systems uniformly asymptotically; and (3) the stability domain is estimated. The conservatism of the results obtained is reduced by full using the system output information. Finally, the numerical examples are given to demonstrate the effectiveness of the results obtained in this paper.