Continuous maintenance and the future – Foundations and technological challenges

High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security

Cyber-Physical Manufacturing Metrology Model (CPM3) - Big Data Analytics Issue

Internet of Things (IoT) is changing the world, and therefore the application of ICT (Information and Communication Technology) in manufacturing. As a paradigm based on the Internet, IoT utilizes the benefits of interrelated technologies/smart devices such as RFID (Radio Frequency Identification) and WSAN (Wireless Sensor and Actuator Networks) for the retrieval and exchange of information thus opening up new possibilities for integration of manufacturing system and its cyber representation through Cyber-Physical Manufacturing (CPM) model. On the other hand, CPM and digital manufacturing represent the key elements for implementation of Industry 4.0 and backbone for "smart factory" generation. Interconnected smart devices generate huge databases (big data), so that Cloud computing becomes indispensable tool to support the CPM. In addition, CPM has an extremely expressed requirement for better control, monitoring and data management. Limitations still exist in storages, networks and computers, as well as in the tools for complex data analysis, detection of its structure and retrieval of useful information. Products, resources, and processes within smart factory are realized and controlled through CPM model. In this context, our recent research efforts in the field of quality control and manufacturing metrology are directed to the development of framework for Cyber-Physical Manufacturing Metrology Model (CPM3). CPM3 framework will be based on: 1) integration of digital product metrology information obtained from big data using BDA (big data analytics) through metrology features recognition, and 2) generation of global/local inspection plan for CMM (Coordinate Measuring Machine) from extracted information. This paper will present recent results of our research on CPM3 - big data analytics issue

Design, modelling, simulation and integration of cyber physical systems: Methods and applications

The main drivers for the development and evolution of Cyber Physical Systems (CPS) are the reduction of development costs and time along with the enhancement of the designed products. The aim of this survey paper is to provide an overview of different types of system and the associated transition process from mechatronics to CPS and cloud-based (IoT) systems. It will further consider the requirement that methodologies for CPS-design should be part of a multi-disciplinary development process within which designers should focus not only on the separate physical and computational components, but also on their integration and interaction. Challenges related to CPS-design are therefore considered in the paper from the perspectives of the physical processes, computation and integration respectively. Illustrative case studies are selected from different system levels starting with the description of the overlaying concept of Cyber Physical Production Systems (CPPSs). The analysis and evaluation of the specific properties of a sub-system using a condition monitoring system, important for the maintenance purposes, is then given for a wind turbine

Definition of the Future Skills Needs of Job Profiles in the Renewable Energy Sector

The growth of the renewable energy industry is happening at a swift pace pushed, by the emergence of Industry 4.0. Smart technologies like artificial intelligence (AI), Big Data, the Internet of Things (IoT), Digital Twin (DT), etc. enable companies within the sector of renewable energies to drastically improve their operations. In this sectoral context, where upgraded sustainability standards also play a vital role, it is necessary to fulfil the human capital requirements of the imminent technological advances. This article aims to determine the current skills of the renewable energy industry workforce and to predict the upcoming skill requirements linked to a digital transition by creating a unified database that contains both types of skills. This will serve as a tool for renewable energy businesses, education centers, and policymakers to plan the training itinerary necessary to close the skills gap, as part of the sectoral strategy to achieve a competent future workforce.This research was partly funded by (a) the European Union through the Erasmus Plus Programme (Grant Agreement No. 2018-3019/001-001, Project No. 600886-1-2018-1-DE-EPPKA2-SSA-B)*, (b) the 4gune cluster, Siemens Gamesa and Aalborg University through the project “Identification of the necessary skills and competences for professionals of the future renewable energy sector”, and (c) Lantek, Inzu Group, Fundación Telefónica and Fundación BBK, partners of the Deusto Digital Industry Chair

Review of Machine Learning Approaches In Fault Diagnosis applied to IoT System

International audienceWith increasing complex systems, low production costs, and changing technologies, for this reason, the automatic fault diagnosis using artificial intelligence (AI) techniques is more in more applied. In addition, with the emergence of the use of reconfigurable systems, AI can assist in self-maintenance of complex systems. The purpose of this article is to summarize the diagnosis research of systems using AI approaches and examine their application particularly in the field of diagnosis of complex systems. It covers articles published from 2002 to 2018 using Machine Learning tools for fault diagnosis in industrial systems

Business analytics in industry 4.0: a systematic review

Recently, the term “Industry 4.0” has emerged to characterize several Information Technology and Communication (ICT) adoptions in production processes (e.g., Internet-of-Things, implementation of digital production support information technologies). Business Analytics is often used within the Industry 4.0, thus incorporating its data intelligence (e.g., statistical analysis, predictive modelling, optimization) expert system component. In this paper, we perform a Systematic Literature Review (SLR) on the usage of Business Analytics within the Industry 4.0 concept, covering a selection of 169 papers obtained from six major scientific publication sources from 2010 to March 2020. The selected papers were first classified in three major types, namely, Practical Application, Reviews and Framework Proposal. Then, we analysed with more detail the practical application studies which were further divided into three main categories of the Gartner analytical maturity model, Descriptive Analytics, Predictive Analytics and Prescriptive Analytics. In particular, we characterized the distinct analytics studies in terms of the industry application and data context used, impact (in terms of their Technology Readiness Level) and selected data modelling method. Our SLR analysis provides a mapping of how data-based Industry 4.0 expert systems are currently used, disclosing also research gaps and future research opportunities.The work of P. Cortez was supported by FCT - Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. We would like to thank to the three anonymous reviewers for their helpful suggestions

A Framework for Industry 4.0

The potential of the Industry 4.0 will allow the national industry to develop all kinds of procedures, especially in terms of competitive differentiation. The prospects and motivations behind Industry 4.0 are related to the management that is essentially geared towards industrial internet, to the integrated analysis and use of data, to the digitalization of products and services, to new disruptive business models and to the cooperation within the value chain. It is through the integration of Cyber-Physical Systems (CPS), into the maintenance process that it is possible to carry out a continuous monitoring of industrial machines, as well as to apply advanced techniques for predictive and proactive maintenance. The present work is based on the MANTIS project, aiming to construct a specific platform for the proactive maintenance of industrial machines, targeting particularly the case of GreenBender ADIRA Steel Sheet. In other words, the aim is to reduce maintenance costs, increase the efficiency of the process and consequently the profit. Essentially, the MANTIS project is a multinational research project, where the CISTER Research Unit plays a key role, particularly in providing the communications infrastructure for one MANTIS Pilot. The methodology is based on a follow-up study, which is jointly carried with the client, as well as within the scope of the implementation of the ADIRA Pilot. The macro phases that are followed in the present work are: 1) detailed analysis of the business needs; 2) preparation of the architecture specification; 3) implementation/development; 4) tests and validation; 5) support; 6) stabilization; 7) corrective and evolutionary maintenance; and 8) final project analysis and corrective measures to be applied in future projects. The expected results of the development of such project are related to the integration of the industrial maintenance process, to the continuous monitoring of the machines and to the application of advanced techniques of preventive and proactive maintenance of industrial machines, particularly based on techniques and good practices of the Software Engineering area and on the integration of Cyber-Physical Systems.O potencial desenvolvido pela Indústria 4.0 dotará a indústria nacional de capacidades para desenvolver todo o tipo de procedimentos, especialmente a nível da diferenciação competitiva. As perspetivas e as motivações por detrás da Indústria 4.0 estão relacionadas com uma gestão essencialmente direcionada para a internet industrial, com uma análise integrada e utilização de dados, com a digitalização de produtos e de serviços, com novos modelos disruptivos de negócio e com uma cooperação horizontal no âmbito da cadeia de valor. É através da integração dos sistemas ciber-físicos no processo de manutenção que é possível proceder a um monitoramento contínuo das máquinas, tal como à aplicação de técnicas avançadas para a manutenção preditiva e pró-ativa das mesmas. O presente trabalho é baseado no projeto MANTIS, objetivando, portanto, a construção de uma plataforma específica para a manutenção pró-ativa das máquinas industriais, neste caso em concreto das prensas, que serão as máquinas industriais analisadas ao longo do presente trabalho. Dito de um outro modo, objetiva-se, através de uma plataforma em específico, reduzir todos os custos da sua manutenção, aumentando, portanto, os lucros industriais advindos da produção. Resumidamente, o projeto MANTIS consiste num projeto de investigação multinacional, onde a Unidade de Investigação CISTER desenvolve um papel fundamental, particularmente no fornecimento da infraestrutura de comunicação no Piloto MANTIS. A metodologia adotada é baseada num estudo de acompanhamento, realizado em conjunto com o cliente, e no âmbito da implementação do Piloto da ADIRA. As macro fases que são compreendidas por esta metodologia, e as quais serão seguidas, são: 1) análise detalhada das necessidades de negócio; 2) preparação da especificação da arquitetura; 3) implementação/desenvolvimento; 4) testes e validação; 5) suporte; 6) estabilização; 7) manutenção corretiva e evolutiva; e 8) análise final do projeto e medidas corretivas a aplicar em projetos futuros. Os resultados esperados com o desenvolvimento do projeto estão relacionados com a integração do processo de manutenção industrial, a monitorização contínua das máquinas e a aplicação de técnicas avançadas de manutenção preventiva e pós-ativa das máquinas, especialmente com base em técnicas e boas práticas da área de Engenharia de Software