Key Contributing Factors to the Acceptance of Agents in Industrial Environments

Multiple software agent-based solutions have been developed during the last decades, and applied with varying success to different domains offering control, reconfiguration, diagnosis, monitoring, etc. However, the promise that they once posed in terms of a new alternative decentralized approach offering modularity, flexibility and robustness, is only partially fulfilled. This paper investigates some key factors, i.e., design, technology, intelligence/algorithms, standardization, hardware, challenges, application and cost, which are hypothesized to be linked to the Industrial Agent acceptance. Empirical data was acquired via a conducted survey, and statistically analyzed to investigate the support of the posed hypotheses. The results indicate that all the factors are seen important issues that play a role toward deciding for or against an industrial agent solution.info:eu-repo/semantics/publishedVersio

Industrial agents in the era of service-oriented architectures and cloudbased industrial infrastructures

The umbrella paradigm underpinning novel collaborative industrial systems is to consider the set of intelligent system units as a conglomerate of distributed, autonomous, intelligent, proactive, fault-tolerant, and reusable units, which operate as a set of cooperating entities (Colombo and Karnouskos, 2009). These entities are forming an evolvable infrastructure, entering and/or going out (plug-in/plugout) in an asynchronous manner. Moreover, these entities, having each of them their own functionalities, data, and associated information are now connected and able to interact. They are capable of working in a proactive manner, initiating collaborative actions and dynamically interacting with each other in order to achieve both local and global objectives.info:eu-repo/semantics/publishedVersio

Industrial automation based on cyber-physical systems technologies: Prototype implementations and challenges

Cyber-Physical Systems (CPS) is an emergent approach that focuses on the integration of computational applications with physical devices, being designed as a network of interacting cyber and physical elements. CPS control and monitor real-world physical infrastructures and thus is starting having a high impact in industrial automation. As such design, implementation and operation of CPS and management of the resulting automation infrastructure is of key importance for the industry. In this work, an overview of key aspects of industrial CPS, their technologies and emerging directions, as well as challenges for their implementation is presented. Based on the hands-on experiences gathered from four European innovation projects over the last decade (i.e. SOCRADES, IMC-AESOP, GRACE and ARUM), a key challenges have been identified and a prioritization and timeline are pointed out with the aim to increase Technology Readiness Levels and lead to their usage in industrial automation environments.The authors would like to thank for their support the European Commission, and the partners of the EU FP6 SOCRADES (www.socrades.net), EU FP7 GRACE (www.grace-project.org), EU FP7 IMC-AESOP (www.imc-aesop.eu) and EU FP7 ARUM (www.arum-project.eu) projects, for their fruitful support and discussions.info:eu-repo/semantics/publishedVersio

A model and an evolutionary algorithmic approach towards optimization of Electric Vehicle fleet charging

Abstract—The prevalence of the Smart Grid and its capabil-ities, has enabled sophisticated energy management that can be realized as a multi-constraint optimization problem and tailored to the specific scenario needs. In conjunction with the increasing introduction of Electric Vehicles (EVs), energy management tools can now consider expanded conditions including grid balance, cost optimization, EV characteristics, asset utilization, operational goals etc. In this work we analyze such a scenario and demonstrate how an EV fleet charging can be optimized in a timely manner while taking into consideration local conditions e.g., individual EV needs as well as global ones e.g., grid limits and energy price. We formalize a model that reflects the EV restrictions, and use it to assess an algorithmic approach that solves this non-linear optimization problem

A Time-Series Compression Technique and its Application to the Smart Grid

Time-series data is increasingly collected in many domains. One example is the smart electricity infrastructure, which generates huge volumes of such data from sources such as smart electricity meters. Although today this data is used for visualization and billing in mostly 15-min resolution, its original temporal resolution frequently is more fine-grained, e.g., seconds. This is useful for various analytical applications such as short-term forecasting, disaggregation and visualization. However, transmitting and storing huge amounts of such fine-grained data is prohibitively expensive in terms of storage space in many cases. In this article, we present a compression technique based on piecewise regression and two methods which describe the performance of the compression. Although our technique is a general approach for time-series compression, smart grids serve as our running example and as our evaluation scenario. Depending on the data and the use-case scenario, the technique compresses data by ratios of up to factor 5,000 while maintaining its usefulness for analytics. The proposed technique has outperformed related work and has been applied to three real-world energy datasets in different scenarios. Finally, we show that the proposed compression technique can be implemented in a state-of-the-art database management system

Service-oriented SCADA and MES supporting petri nets based orchestrated automation systems

The fusion of mechatronics, communication, control and information technologies has allowed the introduction of new automation paradigms into the production environment. The virtualization of the production environment facilitated by the application of the service-oriented architecture paradigm is one of major outcomes of that fusion. On one side, service-oriented automation works based on exposition, subscription and use of automation functions represented by e.g. web services. On the other side, the evolution of traditional industrial systems, particularly in the production area, as a response to architectural and behavioural (functional) viewpoints of the ISA95 enterprise architecture, where a close inter-relation between SCADA, DCS and MES systems facilitate the management and control of the production environment. Automation functions are increasingly performed by the composition and orchestration of services. Among other methods, the application of formal Petri net based orchestration approaches is being industrially established. This paper presents the major characteristics that such a Petri net based orchestration presents when it is developed, implemented and deployed in an industrial environmentThe research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement 258682 (IMC-AESOP: ArchitecturE for Service-Oriented Process - Monitoring and Control) and 224053 (CONET: Cooperating Objects NETwork of excellence)

Quo vadis industry 4.0? Position, trends, and challenges

Industry 4.0 vision and its mandated digital transformation are radically reshaping the way business is carried out and the way overall industrial processes and collaborations are operating. In this work, the objective is to analyze the current level of adoption of Industry 4.0, via the footprint available in industrial and academic research works. The analysis performed reveals insights on how Industry 4.0 has impacted and is still influencing research and innovation in industrial systems, services, and business approaches. It also reveals pertinent trends on key enabling features, technologies and challenges associated with this 4th industrial revolution, mainly focusing on the pathways for wider industrial adoption of Industry 4.0-compliant technologies and solutions.info:eu-repo/semantics/publishedVersio

Cross benefits from cyber-physical systems and intelligent products for future smart industries

The manufacturing industry is facing a technology paradigm change, as also captured in the Industrie 4.0 vision as the fourth industrial revolution. Future smart industries will require to optimize not only their own manufacturing processes but also the use of products and manufacturing resources, their maintenance and their recycling. In this context the strengths and weak nesses of two key concepts, namely Cyber-Physical Systems (CPS) and Intelligent Product (IP) are discussed, and it is suggested that an integration of these two approaches to meet the introduced emergent requirements is beneficial. The integration of CPS and IP is shown via two real-world industrial cases, covering different phases of the product life-cycle, namely the production, use and maintenance phases.Surferl@b is partially funded by ERDF (European Regional Development Fund), and the authors wish to thank the European Union and the Nord-Pas -de-Calais in France region for their support. This work is also (partially) funded by the Operational Program for Competitiveness and Internationalization – COMPETE 2020 and by FCT – Portuguese Foundation for Science and Technology.info:eu-repo/semantics/publishedVersio

Engineering of next generation cyber-physical automation system architectures

Cyber-Physical-Systems (CPS) enable flexible and reconfigurable realization of automation system architectures, utilizing distributed control architectures with non-hierarchical modules linked together through different communication systems. Several control system architectures have been developed and validated in the past years by research groups. However, there is still a lack of implementation in industry. The intention of this work is to provide a summary of current alternative control system architectures that could be applied in industrial automation domain as well as a review of their commonalities. The aim is to point out the differences between the traditional centralized and hierarchical architectures to discussed ones, which rely on decentralized decision-making and control. Challenges and impacts that industries and engineers face in the process of adopting decentralized control architectures are discussed, analysing the obstacles for industrial acceptance and the new necessary interdisciplinary engineering skills. Finally, an outlook of possible mitigation and migration actions required to implement the decentralized control architectures is addressed.The authors would like to thank the European Commission for the support, and the partners of the EU Horizon 2020 project PERFoRM (2016b) for the fruitful discussions. The PERFoRM project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 680435.info:eu-repo/semantics/publishedVersio