IEEE Access Special Section: Cyber-Physical Systems

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Increase the adoption of Agent-based Cyber-Physical Production Systems through the Design of Minimally Invasive Solutions

During the last few years, many approaches were proposed to offer companies the ability to have dynamic and flexible production systems. One of the conventional ap-proaches to solving this problem is the implementation of cyber-physical production sys-tems using multi-agent distributed systems. Although these systems can deal with several challenges faced by companies in this area, they have not been accepted and used in real cases. In this way, the primary objective of the proposed work is to understand the chal-lenges usually found in the adoption of these solutions and to develop a strategy to in-crease their acceptance and implementation. Thus, the document focuses on the design and development of cyber-physical produc-tion systems based on agent approaches, requiring minimal changes in the existing pro-duction systems. This approach aims of reducing the impact and the alterations needed to adopt those new cyber-physical production systems. Clarifying the subject, the author presents a definition of a minimal invasive agent-based cyber-physical production system and, the functional requirements that the designers and developers must respect to imple-ment the new software. From these functional requirements derived a list of design princi-ples that must be fulfilled to design and develop a system with these characteristics. Subsequently, to evaluate solutions that aim to be minimally invasive, an evaluation model based on a fuzzy inference system is proposed, which rank the approaches accord-ing to each of the design principles and globally. In this way, the proposed work presents the functional requirements, design principles and evaluation model of minimally invasive cyber-physical production systems, to increase the adoption of such systems

Cooperative Human-Machine Interaction in Industrial Environments

Until the present days, there has been little advances in the relation between the shop-floor operator in an industrial environment and the machines execution the manufacturing processes. Normally, the semi-automatic processes for collaborative assembly in industry are composed of a human and non-human elements. In the human perspective, one or more persons can be working in the same cell directly or indirectly with a non-human entity. In a cell can exist several machines, normally robotic arms that perform very specific collaborative tasks with the operators. However, the latest advances are mostly related with security issues and regulations, like immediately stopping the machine if a human touches it, and not much related with operative issues like adjusting the process velocity (within a certain window of cycle time) or give preference to some tasks over another in the beginning of the shift, to benefit the operator's working conditions. Therefore, a step forward to a more advanced interaction between machine and operator should be taken, towards a more adaptive and rich symbiosis. The main goal of the present Dissertation is to explore the relation between the shop-floor operator and the machine in a cyber physical system. For that purpose, biometric sensors will be used (ECG, EMG, EDA, PZT, wearables and others) to monitor the operators physiology during the operative times, and based on that, explore how a collaborative process can be adapted to minimize the operator's stress and fatigue. First, the correct set of sensors should be explored to understand how stress and fatigue metrics can be calculated. Secondly, optimization techniques need to be studied in order to, e.g. finds the correct machine's process parameterization that, on one hand, minimizes the operator's fatigue and stress, and on the other, do not jeopardizes the process requirements in terms of timing and quality. Therefore, this can be stated as a multivariate optimization problem

Cyber-Physical Power System (CPPS): A Review on Modelling, Simulation, and Analysis with Cyber Security Applications

Cyber-Physical System (CPS) is a new kind of digital technology that increases its attention across academia, government, and industry sectors and covers a wide range of applications like agriculture, energy, medical, transportation, etc. The traditional power systems with physical equipment as a core element are more integrated with information and communication technology, which evolves into the Cyber-Physical Power System (CPPS). The CPPS consists of a physical system tightly integrated with cyber systems (control, computing, and communication functions) and allows the two-way flows of electricity and information for enabling smart grid technologies. Even though the digital technologies monitoring and controlling the electric power grid more efficiently and reliably, the power grid is vulnerable to cybersecurity risk and involves the complex interdependency between cyber and physical systems. Analyzing and resolving the problems in CPPS needs the modelling methods and systematic investigation of a complex interaction between cyber and physical systems. The conventional way of modelling, simulation, and analysis involves the separation of physical domain and cyber domain, which is not suitable for the modern CPPS. Therefore, an integrated framework needed to analyze the practical scenario of the unification of physical and cyber systems. A comprehensive review of different modelling, simulation, and analysis methods and different types of cyber-attacks, cybersecurity measures for modern CPPS is explored in this paper. A review of different types of cyber-attack detection and mitigation control schemes for the practical power system is presented in this paper. The status of the research in CPPS around the world and a new path for recommendations and research directions for the researchers working in the CPPS are finally presented.publishedVersio

New Trends in the Use of Artificial Intelligence for the Industry 4.0

Industry 4.0 is based on the cyber-physical transformation of processes, systems and methods applied in the manufacturing sector, and on its autonomous and decentralized operation. Industry 4.0 reflects that the industrial world is at the beginning of the so-called Fourth Industrial Revolution, characterized by a massive interconnection of assets and the integration of human operators with the manufacturing environment. In this regard, data analytics and, specifically, the artificial intelligence is the vehicular technology towards the next generation of smart factories.Chapters in this book cover a diversity of current and new developments in the use of artificial intelligence on the industrial sector seen from the fourth industrial revolution point of view, namely, cyber-physical applications, artificial intelligence technologies and tools, Industrial Internet of Things and data analytics. This book contains high-quality chapters containing original research results and literature review of exceptional merit. Thus, it is in the aim of the book to contribute to the literature of the topic in this regard and let the readers know current and new trends in the use of artificial intelligence for the Industry 4.0

Reliable Control Applications with Wireless Communication Technologies: Application to Robotic Systems

The nature of wireless propagation may reduce the QoS of the applications, such that some packages can be delayed or lost. For this reason, the design of wireless control applications must be faced in a holistic way to avoid degrading the performance of the control algorithms. This paper is aimed at improving the reliability of wireless control applications in the event of communication degradation or temporary loss at the wireless links. Two controller levels are used: sophisticated algorithms providing better performance are executed in a central node, whereas local independent controllers, implemented as back-up controllers, are executed next to the process in case of QoS degradation. This work presents a reliable strategy for switching between central and local controllers avoiding that plants may become uncontrolled. For validation purposes, the presented approach was used to control a planar robot. A Fuzzy Logic control algorithm was implemented as a main controller at a high performance computing platform. A back-up controller was implemented on an edge device. This approach avoids the robot becoming uncontrolled in case of communication failure. Although a planar robot was chosen in this work, the presented approach may be extended to other processes. XBee 900 MHz communication technology was selected for control tasks, leaving the 2.4 GHz band for integration with cloud services. Several experiments are presented to analyze the behavior of the control application under different circumstances. The results proved that our approach allows the use of wireless communications, even in critical control applications.This research was funded by the Basque Government through the project EKOHEGAZ (ELKARTEK KK-2021/00092), by Diputación Foral de Álava (DFA) through the project CONAVANTER, and by UPV/EHU through the project GIU20/063

An Industrial Data Analysis and Supervision Framework for Predictive Manufacturing Systems

Due to the advancements in the Information and Communication Technologies field in the modern interconnected world, the manufacturing industry is becoming a more and more data rich environment, with large volumes of data being generated on a daily basis, thus presenting a new set of opportunities to be explored towards improving the efficiency and quality of production processes. This can be done through the development of the so called Predictive Manufacturing Systems. These systems aim to improve manufacturing processes through a combination of concepts such as Cyber-Physical Production Systems, Machine Learning and real-time Data Analytics in order to predict future states and events in production. This can be used in a wide array of applications, including predictive maintenance policies, improving quality control through the early detection of faults and defects or optimize energy consumption, to name a few. Therefore, the research efforts presented in this document focus on the design and development of a generic framework to guide the implementation of predictive manufacturing systems through a set of common requirements and components. This approach aims to enable manufacturers to extract, analyse, interpret and transform their data into actionable knowledge that can be leveraged into a business advantage. To this end a list of goals, functional and non-functional requirements is defined for these systems based on a thorough literature review and empirical knowledge. Subsequently the Intelligent Data Analysis and Real-Time Supervision (IDARTS) framework is proposed, along with a detailed description of each of its main components. Finally, a pilot implementation is presented for each of this components, followed by the demonstration of the proposed framework in three different scenarios including several use cases in varied real-world industrial areas. In this way the proposed work aims to provide a common foundation for the full realization of Predictive Manufacturing Systems

Advances in Theoretical and Computational Energy Optimization Processes

The paradigm in the design of all human activity that requires energy for its development must change from the past. We must change the processes of product manufacturing and functional services. This is necessary in order to mitigate the ecological footprint of man on the Earth, which cannot be considered as a resource with infinite capacities. To do this, every single process must be analyzed and modified, with the aim of decarbonising each production sector. This collection of articles has been assembled to provide ideas and new broad-spectrum contributions for these purposes