4,272 research outputs found
Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor
The increasing demand of customized production results in huge challenges to the traditional manufacturing systems. In order to allocate resources timely according to the production requirements and to reduce disturbances, a framework for the future intelligent shopfloor is proposed in this paper. The framework consists of three primary models, namely the model of smart machine agent, the self-organizing model, and the self-adaptive model. A cyber-physical system for manufacturing shopfloor based on the multiagent technology is developed to realize the above-mentioned function models. Gray relational analysis and the hierarchy conflict resolution methods were applied to achieve the self-organizing and self-adaptive capabilities, thereby improving the reconfigurability and responsiveness of the shopfloor. A prototype system is developed, which has the adequate flexibility and robustness to configure resources and to deal with disturbances effectively. This research provides a feasible method for designing an autonomous factory with exception-handling capabilities
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
Digitization of industrial environments through an industry 4.0 compliant approach
About a decade after the introduction of Industry
4.0 (I4.0) as a paradigm oriented towards the digitization
of industrial environments, centered on the concept of industrial
Cyber-physical Systems (CPS) to enable the development of
intelligent and distributed industrial systems, many companies
around the world are still not immersed in this digital
transformation era. This transition is not straightforward and
requires the aligned with the novel technologies, architectures
and standards to migrate entire traditional systems into I4.0
systems. In this context, this paper presents an approach to
perform the digitization of non-I4.0 components/systems into I4.0
through an approach based on the Asset Administration Shell
(AAS), which is a standardized digital representation of an asset.
This approach enables to hold the asset information throughout
its lifecycle, provides a standard communication interface with
the asset, and is based on a set of modules that are combined
with the AAS to provide novel functionalities for the asset, e.g.,
monitoring, diagnosis and optimization. Moreover, this approach
adopts Multi-agent Systems (MAS) to provide mainly autonomy
and collaborative capabilities to the system. The agents are able to
get information from the AASs, making intelligent decisions and
perform distributed tasks following interaction strategies, e.g.,
collaboration, negotiation and self-organization. The feasibility
of the proposed approach was tested by digitizing a small-scale
production system comprising several assets.The authors are grateful to the Foundation for Science
and Technology (FCT), Portugal, for financial support
through national funds FCT/MCTES (PIDDAC) to CeDRI
(UIDB/05757/2020 and UIDP/05757/2020) and SusTEC
(LA/P/0007/2021). The author Lucas Sakurada thanks the FCT
for the PhD Grant 2020.09234.BD.info:eu-repo/semantics/publishedVersio
A sequential decision problem formulation and deep reinforcement learning solution of the optimization of O&M of cyber-physical energy systems (CPESs) for reliable and safe power production and supply
The Operation & Maintenance (O&M) of Cyber-Physical Energy Systems (CPESs) is driven by reliable and safe production and supply, that need to account for flexibility to respond to the uncertainty in energy demand and also supply due to the stochasticity of Renewable Energy Sources (RESs); at the same time, accidents of severe consequences must be avoided for safety reasons. In this paper, we consider O&M strategies for CPES reliable and safe production and supply, and develop a Deep Reinforcement Learning (DRL) approach to search for the best strategy, considering the system components health conditions, their Remaining Useful Life (RUL), and possible accident scenarios. The approach integrates Proximal Policy Optimization (PPO) and Imitation Learning (IL) for training RL agent, with a CPES model that embeds the components RUL estimator and their failure process model. The novelty of the work lies in i) taking production plan into O&M decisions to implement maintenance and operate flexibly; ii) embedding the reliability model into CPES model to recognize safety related components and set proper maintenance RUL thresholds. An application, the Advanced Lead-cooled Fast Reactor European Demonstrator (ALFRED), is provided. The optimal solution found by DRL is shown to outperform those provided by state-of-the-art O&M policies
Internet of things: Conceptual network structure, main challenges and future directions
Internet of Things (IoT) is a key technology trend that supports our digitalized society in applications such as
smart countries and smart cities. In this study, we investigate the existing strategic themes, thematic evolution
structure, key challenges, and potential research opportunities associated with the IoT. For this study, we conduct
a Bibliometric Performance and Network Analysis (BPNA), supplemented by an exhaustive Systematic Literature
Review (SLR). Specifically, in BPNA, the software SciMAT is used to analyze 14,385 documents and 30,381
keywords in the Web of Science (WoS) database, which was released between 2002 and 2019. The results reveal
that 31 clusters are classified according to their importance and development, and the conceptual structures of
key clusters are presented, along with their performance analysis and the relationship with other subthemes. The
thematic evolution structure describes the important cluster(s) over time. For the SLR, 23 documents are
analyzed. The SLR reveals key challenges and limitations associated with the IoT. We expect the results will form
the basis of future research and guide decision-making in the IoT and other supporting industries.Coordenaç~ao de Aperfeiçoamento
de Pessoal de NÃvel Superior - Brazil (CAPES) - Finance Code 001
and the Spanish Ministry of Science and Innovation under grants
PID2019-105381 GA-100 (iScience)Consejo
Nacional de Ciencia y TecnologÃa (CONACYT) and Direcci on General de
Relaciones Exteriores (DGRI
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