Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Intelligent and predictive maintenance in manufacturing systems

In recent years manufacturing companies have been facing a major shift in the manufacturing requirements, for example the shift in demand for highly customized products resulting in a shorter product life cycle, rather than the traditional mass production of standardized products. As a consequence of the change, the enterprises are facing the need to adapt, forcing all sectors of the manufacturing activity to move accordingly. Maintenance is one of the major activities in manufacturing as it highly influences production productivity and quality, and has a direct impact on production cost and customer satisfaction. Nowadays, corrective and scheduled maintenance are widely implemented. However, the manufacturing world need to adapt to this new reality by implementing new, intelligent and innovative maintenance systems capable of predicting in advance possible failures. Lately, predictive maintenance systems and tools have been developed and continue to be studied and improved. However, companies do not have enough trust on these systems to fully rely on them. Considering all these aspects, the work developed on this thesis introduces a system architecture for an intelligent predictive maintenance system based on the Condition-Based Maintenance (CBM) to be used in the Catraport case study, focusing particularly on the development of the monitoring module of the system architecture. This module comprises a tool developed by using Node-RED that displays the collected data alongside with the warnings triggered by cross-checking the incoming data with implemented decision rules, through the use of graphics and text. Additionally, an Android mobile application was also developed to allow consulting remotely the operating state of the assets.Nos últimos anos, as empresas de manufatura têm enfrentado uma grande mudança nos requisitos de fabrico, nomeadamente, na procura por produtos altamente personalizados, resultando num ciclo de vida do produto mais curto, contrariamente à tradicional produção em massa de produtos padronizados. Como consequência desta mudança, as empresas, bem como todos os setores da atividade de manufatura, enfrentam a necessidade de se adaptar. A manutenção é uma das principais atividades de fabrico, visto que influência fortemente a produtividade e a qualidade da produção, e tem um impacto direto no custo do produto e na satisfação do cliente. Atualmente, as estratégias de manutenção corretiva e programada são amplamente implementadas. No entanto, o mundo da manufatura precisa de se adaptar à nova realidade, implementando sistemas de manutenção novos, inteligentes e inovadores, capazes de prever possíveis falhas. Ultimamente, os sistemas e ferramentas de manutenção preditiva têm sido desenvolvidos e continuam a ser estudados e melhorados. No entanto, as empresas não possuem confiança suficiente nesses sistemas para os implementar nas suas instalações. Considerando todos esses aspetos, o trabalho desenvolvido nesta dissertação introduz uma arquitetura para um sistema inteligente de manutenção preditiva baseado na técnica Condition- Based Maintenance (CBM) a ser usado no estudo de caso da Catraport, focando-se particularmente no desenvolvimento do módulo de monitorização da arquitetura. Este módulo compreende uma ferramenta desenvolvida com recurso ao Node-RED que exibe os dados colecionados. Adicionalmente são apresentados avisos originados pelo cruzamento dos dados recebidos com as regras de decisão implementadas. Além disso, uma aplicação móvel Android também foi desenvolvida para permitir a consulta remota o estado operacional dos equipamentos

Integration of Industry 4.0 to the CBM practices of the O&G upstream sector in Nigeria

Purpose This study investigates the integration of Industry 4.0 (I4.0) technologies with condition-based maintenance (CBM) in upstream oil and gas (O&G) operations, focussing on developing countries like Nigeria. The research identifies barriers to this integration and suggests solutions, intending to provide practical insights for improving operational efficiency in the O&G sector. Design/methodology/approach The study commenced with an exhaustive review of extant literature to identify existing barriers to I4.0 implementation and contextualise the study. Subsequent to this foundational step, primary data are gathered through the administration of carefully constructed questionnaires targeted at professionals specialised in maintenance within the upstream O&G sector. A semi-structured interview was also conducted to elicit more nuanced, contextual insights from these professionals. Analytically, the collected data were subjected to descriptive statistical methods for summarisation and interpretation with a measurement model to define the relationships between observed variables and latent construct. Moreover, the Relative Importance Index was utilised to systematically prioritise and rank the key barriers to I4.0 integration to CBM within the upstream O&G upstream sector. Findings The most ranked obstacles in integrating I4.0 technologies to the CBM strategy in the O&G industry are lack of budget and finance, limited engineering and technological resources, lack of support from executives and leaders of the organisations and lack of competence. Even though the journey of digitalisation has commenced in the O&G industry, there are limited studies in this area. Originality/value The study serves as both an academic cornerstone and a practical guide for the operational integration of I4.0 technologies within Nigeria's O&G upstream sector. Specifically, it provides an exhaustive analysis of the obstacles impeding effective incorporation into CBM practices. Additionally, the study contributes actionable insights for industry stakeholders to enhance overall performance and achieve key performance indices (KPIs)

Is Ambient Intelligence a truly Human-Centric Paradigm in Industry? Current Research and Application Scenario

The use of pervasive networked devices is nowadays a reality in the service sector. It impacts almost all aspects of our daily lives, although most times we are not aware of its influence. This is a fundamental characteristic of the concept of Ambient Intelligence (AmI). Ambient Intelligence aims to change the form of human-computer interaction, focusing on the user needs so they can interact in a more seamless way, with emphasis on greater user-friendliness. The idea of recognizing people and their context situation is not new and has been successfully applied with limitations, for instance, in the health and military sectors. However its appearance in the manufacturing industry has been elusive. Could the concept of AmI turn the current shop floor into a truly human centric environment enabling comprehensive reaction to human presence and action? In this article an AmI scenario is presented and detailed with applications in human’s integrity and safety.Ambient Intelligence, networks, human-computer interaction

VANET Applications: Hot Use Cases

Current challenges of car manufacturers are to make roads safe, to achieve free flowing traffic with few congestions, and to reduce pollution by an effective fuel use. To reach these goals, many improvements are performed in-car, but more and more approaches rely on connected cars with communication capabilities between cars, with an infrastructure, or with IoT devices. Monitoring and coordinating vehicles allow then to compute intelligent ways of transportation. Connected cars have introduced a new way of thinking cars - not only as a mean for a driver to go from A to B, but as smart cars - a user extension like the smartphone today. In this report, we introduce concepts and specific vocabulary in order to classify current innovations or ideas on the emerging topic of smart car. We present a graphical categorization showing this evolution in function of the societal evolution. Different perspectives are adopted: a vehicle-centric view, a vehicle-network view, and a user-centric view; described by simple and complex use-cases and illustrated by a list of emerging and current projects from the academic and industrial worlds. We identified an empty space in innovation between the user and his car: paradoxically even if they are both in interaction, they are separated through different application uses. Future challenge is to interlace social concerns of the user within an intelligent and efficient driving

Anthropocentric perspective of production before and within Industry 4.0

Abstract This paper presents a systematic literature review (SLR) of the anthropocentric perspective of production before and after (or, better, within) Industry 4.0. We identify central research clusters regarding traditional Anthropocentric Production Systems (APS) and Anthropocentric Cyber Physical Production Systems. By comparing the two perspectives, we are able to analyse new emerging paradigms in anthropocentric production caused by Industry 4.0. We further make prediction of the future role of the human operator, his needed knowledge and capabilities and how assistance systems support the Operator 4.0. Our paper gives a brief outlook of current and needed future research. It builds grounds for further scholarly discussion on the role of humans in the factory of the future