A Pilot for Proactive Maintenance in Industry 4.0

13th IEEE International Workshop on Factory Communication Systems (WFCS 2017). 31, May to 2, Jun, 2017. Trondheim, Norway.The reliability and safety of industrial machines depends on their timely maintenance. The integration of Cyber Physical Systems within the maintenance process enables both continuous machine monitoring and the application of advanced techniques for predictive and proactive machine maintenance. The building blocks for this revolution – embedded sensors, efficient preprocessing capabilities, ubiquitous connection to the internet, cloud-based analysis of the data, prediction algorithms, and advanced visualization methods – are already in place, but several hurdles have to be overcome to enable their application in real scenarios, namely: the integration with existing machines and existing maintenance processes. Current research and development efforts are building pilots and prototypes to demonstrate the feasibility and the merits of advanced maintenance techniques, and this paper describes a system for the industrial maintenance of sheet metal working machineryinfo:eu-repo/semantics/publishedVersio

Current advancements on maintenance for household appliances

This communication describes research and development directions that are being explored for the creation of a maintenance system for home appliances. The solution will enable a faster and more accurate automation of after-sale services for home appliances. It will be based on three tiers: data acquisition, data analysis and business tiers. The communication provides a context for the work, both regarding the application area in general and the three tiers, and then describes the objectives of the system being designed.info:eu-repo/semantics/publishedVersio

Automated anomalies detection in the work of industrial robots

This article describes the results of the anomalies automated detection algorithm development in the operation of industrial robots. The development of robotic systems, in particular, industrial robots, and software for them is ahead of the tracking and managing technologies development. The operation of the digital production system involves the generation of a large amount of various data characterizing the state of both the specific equipment and the industrial system as a whole. Such a system produces a sufficient amount of data to develop machine learning models to analyze this data to solve problems such as forecasting and modeling. As part of the study, an experiment was conducted based on the equipment of the laboratory of industrial robots of Tomsk Polytechnic University. In the course of the research, the industrial manipulator moved loads belonging to different classes by weight. An algorithm was developed for the automated analysis of the values of the parameters of the consumed current and the position of the manipulator

Automated anomalies detection in the work of industrial robots

This article describes the results of the anomalies automated detection algorithm development in the operation of industrial robots. The development of robotic systems, in particular, industrial robots, and software for them is ahead of the tracking and managing technologies development. The operation of the digital production system involves the generation of a large amount of various data characterizing the state of both the specific equipment and the industrial system as a whole. Such a system produces a sufficient amount of data to develop machine learning models to analyze this data to solve problems such as forecasting and modeling. As part of the study, an experiment was conducted based on the equipment of the laboratory of industrial robots of Tomsk Polytechnic University. In the course of the research, the industrial manipulator moved loads belonging to different classes by weight. An algorithm was developed for the automated analysis of the values of the parameters of the consumed current and the position of the manipulator

Blockchain Technology Helps Maintenance to Stop Climate Change

The development and interest in Industry 4.0 together with rapid development of Cyber Physical Systems has created magnificent opportunities to develop maintenance to a totally new level. The Maintenance 4.0 vision considers massive exploitation of information regarding factories and machines to improve maintenance efficiency and efficacy, for example by facilitating logistics of spare parts, but on the other hand this creates other logistics issues on the data itself, which only exacerbate data management issues that emerge when distributed maintenance platforms scale up. In fact, factories can be delocalized with respect to the data centers, where data has to be transferred to be processed. Moreover, any transaction needs communication, be it related to purchase of spare parts, sales contract, and decisions making in general, and it has to be verified by remote parties. Keeping in mind the current average level of Overall Equipment Efficiency (50%) i.e. there is a hidden factory behind every factory, the potential is huge. It is expected that most of this potential can be realised based on the use of the above named technologies, and relying on a new approach called blockchain technology, the latter aimed at facilitating data and transactions management. Blockchain supports logistics by a distributed ledger to record transactions in a verifiable and permanent way, thus removing the need for multiple remote parties to verify and store every transaction made, in agreement with the first “r” of maintenance (reduce, repair, reuse, recycle). Keeping in mind the total industrial influence on the climate change, we can expect that with the aid of the new advancements the climate change can be if not totally stopped at least reduced, and contribute to the green economy that Europe aims for. The paper introduces the novel technologies that can support sustainability of manufacturing and industry at large, and proposes an architecture to bind together said technologies to realise the vision of Maintenance 4.0.info:eu-repo/semantics/publishedVersio

Remote maintenance support with the aid of cyber-physical systems and cloud technology

This article discusses how a business model based on traditional maintenance can evolve to generate servitization strategies, with the help of remote maintenance support. The application of cyber-physical systems and cloud technologies play a key role for such maintenance purposes. In fact, the utilization of large quantities of data collected on machines and their processing by means of advanced techniques such as machine learning enable novel techniques for condition-based maintenance. New sensor solutions that could be used in maintenance and interaction with cyber-physical systems are also presented. Here, data models are an important part of these techniques because of the huge amounts of data that are produced and should be processed. These data models have been used in a real case, supported by the Machinery Information Management Open System Alliance Open System Architecture for Condition-Based Maintenance standard architecture, for streamlining the modeling of collected data. In this context, an industrial use case is described, to enlighten the application of the presented concepts in a working pilot. Finally, current and future directions for application of cyber-physical systems and cloud technologies to maintenance are discussed

Requirements for an Intelligent Maintenance System for Industry 4.0

comprobación paso "titulo publicación " - Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future[EN] Recent advances in the development of technological devices and software for Industry 4.0 have pushed a change in the maintenance management systems and processes. Nowadays, in order to maintain a company competitive, a computerised management system is required to help in its maintenance tasks. This paper presents an analysis of the complexities and requirements for maintenance of Industry 4.0. It focuses on intelligent systems that can help to improve the intelligent management of maintenance. Finally, it presents a summary of lessons learned specified as guidelines for the design of such intelligent systems that can be applied horizontally to any company in the Industry.This work is supported by the FEDER/Ministry of Science, Innovation and Universities - State Research Agency RTC-2017-6401-7Garcia, E.; Araujo, A.; Palanca Cámara, J.; Giret Boggino, AS.; Julian Inglada, VJ.; Botti, V. (2019). Requirements for an Intelligent Maintenance System for Industry 4.0. Springer. 340-351. https://doi.org/10.1007/978-3-030-27477-1_26S340351CEN, European Committee for Standardization: EN 13306:2017. Maintenance Terminology. European Standard (2017)Chen, B., Wan, J., Shu, L., Li, P., Mukherjee, M., Yin, B.: Smart factory of Industry 4.0: key technologies, application case, and challenges. IEEE Access 6, 6505–6519 (2018). https://doi.org/10.1109/access.2017.2783682Crespo Marquez, A., Gupta, J.N.: Contemporary maintenance management: process, framework and supporting pillars. Omega 34(3), 313–326 (2006). https://doi.org/10.1016/j.omega.2004.11.003Ferreira, L.L., Albano, M., Silva, J., Martinho, D., Marreiros, G., di Orio, G., Malo, P., Ferreira, H.: A pilot for proactive maintenance in Industry 4.0. In: 2017 IEEE 13th International Workshop on Factory Communication Systems (WFCS). IEEE (2017). https://doi.org/10.1109/wfcs.2017.7991952Goh, K., Tjahjono, B., Baines, T., Subramaniam, S.: A review of research in manufacturing prognostics. In: 2006 IEEE International Conference on Industrial Informatics, Singapore, pp. 417–422. IEEE (2006). https://doi.org/10.1109/INDIN.2006.275836Hashemian, H.M., Bean, W.C.: State-of-the-art predictive maintenance techniques. IEEE Trans. Instrum. Meas. 60(10), 3480–3492 (2011). https://doi.org/10.1109/TIM.2009.2036347Lee, W.J., Wu, H., Yun, H., Kim, H., Jun, M.B., Sutheralnd, J.W.: Predictive maintenance of machine tool systems using artificial intelligence techniques applied to machine condition data. Procedia CIRP 80, 506–511 (2019)Lu, B., Durocher, D., Stemper, P.: Predictive maintenance techniques. IEEE Ind. Appl. Mag. 15(6), 52–60 (2009). https://doi.org/10.1109/MIAS.2009.934444Mrugalska, B., Wyrwicka, M.K.: Towards lean production in Industry 4.0. Procedia Eng. 182, 466–473 (2017). https://doi.org/10.1016/j.proeng.2017.03.135O’Donoghue, C., Prendergast, J.: Implementation and benefits of introducing a computerised maintenance management system into a textile manufacturing company. J. Mater. Process. Technol. 153, 226–232 (2004)Paolanti, M., Romeo, L., Felicetti, A., Mancini, A., Frontoni, E., Loncarski, J.: Machine learning approach for predictive maintenance in Industry 4.0. In: 2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA). IEEE (2018). https://doi.org/10.1109/mesa.2018.8449150Patil, R.B., Mhamane, D.A., Kothavale, P.B., Kothavale, B.: Fault tree analysis: a case study from machine tool industry. Available at SSRN 3382241 (2018)Potes Ruiz, P.A., Kamsu-Foguem, B., Noyes, D.: Knowledge reuse integrating the collaboration from experts in industrial maintenance management. Knowl. Based Syst. 50, 171–186 (2013). https://doi.org/10.1016/j.knosys.2013.06.005Razmi-Farooji, A., Kropsu-Vehkaperä, H., Härkönen, J., Haapasalo, H.: Advantages and potential challenges of data management in e-maintenance. J. Qual. Maint. Eng. (2019)Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Harnisch, M.: Industry 4.0: the future of productivity and growth in manufacturing industries. Boston Consult. Group 9(1), 54–89 (2015)Wan, J., Tang, S., Li, D., Wang, S., Liu, C., Abbas, H., Vasilakos, A.V.: A manufacturing big data solution for active preventive maintenance. IEEE Trans. Ind. Inform. 13(4), 2039–2047 (2017). https://doi.org/10.1109/tii.2017.267050

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

The challenges affecting the reliability and maintainability of rolling stock operating in the Thabazimbi channel

Abstract: Rail transportation remains one of the cheapest and most effective modes of transportation in the Southern African Development Community. The proficiency and capability of the railway system, however, requires capital investment in infrastructure, transport system and, more importantly, rail infrastructure to support socio-economic growth and regional interlink across African nations. To accelerate socio-economic growth in the Southern African Development Community, there is a need for the African countries to work together. This is done with the intention of improving progress by minimising the cost of doing business through local integration and management...M.Phil. (Engineering Management

CPPS retrofitting no contexto da ind?stria 4.0.

Programa de P?s-Gradua??o em Ci?ncia da Computa??o. Departamento de Ci?ncia da Computa??o, Instituto de Ci?ncias Exatas e Biol?gicas, Universidade Federal de Ouro Preto.A Ind?stria 4.0 ?e a nova revolu??o industrial, que envolve a introdu??o de novas tecnologias no ?mbito industrial. No entanto, mudar o patamar tecnol?gico de uma ind?stria desatualizada n?o ? uma tarefa simples. Ao inv?s de trocar todos equipamentos antigos por equipamentos novos nas ind?strias, o conceito retrofitting surge como uma solu??o r?pida e de baixo custo, que visa ao reaproveitamento do equipamento existente, com adi??o de novas tecnologias. Todavia, o retrofitting sofre varia??o de acordo com o tipo e modelo do equipamento industrial, dificultando a atualiza??o de um equipamento industrial para um Sistema de Produ??o Ciber-f?sico ou Cyber-Physical Production System (CPPS). Nesta pesquisa, propomos m?todos para utiliza??o do retrofitting para transforma??o de um equipamento industrial ultrapassado tecnologicamente em um CPPS. A moderniza??o ? feita com o suporte de uma plataforma que possui recursos para integrar o equipamento industrial com a Ind?stria 4.0. Para implementar a plataforma, definimos os requisitos, componentes e tecnologias necess?rios para realiza??o do retrofitting. Todo o procedimento ? realizado com base no Modelo Arquitetural de Refer?ncia para Ind?stria 4.0 - Reference Architectural Model for Industrie 4.0 (RAMI 4.0), uma arquitetura j? difundida da Ind?stria 4.0. Para validar o funcionamento da plataforma, foram realizados estudos de casos com um bra?o rob?tico muito comum em uma ind?stria automatizada e com uma planta did?tica automatizada. Como resultado, mostramos o impacto ap?s o processo que chamamos de CPPS RetrofittingIndustry 4.0 is the new industrial revolution involving the introduction of new technologies in the industrial field. However, changing the technological level of an outdated industry is not a simple task. By retrofitting all old equipment into new equipment in industries, the retrofitting concept emerges as a rapid and low-cost solution, aimed at reusing existing equipment, with the addition of new technologies. However, retrofitting changes according to the type and model of industrial equipment, making it challenging to upgrade industrial equipment to Cyber-Physical Production System (CPPS). In this research, we propose methods to use retrofitting to transform old industrial equipment into a CPPS. The modernization is done with the support of a platform that has resources to integrate industrial equipment with Industry 4.0. To implement the platform, we define the requirements, components, and technologies necessary to retrofit industrial equipment. The entire process is based on Reference Architectural Model for Industrie 4.0 (RAMI 4.0) a widespread architecture of Industry 4.0. With the retrofitting platform based on RAMI 4.0, makes consistent the process of upgrading industrial equipment, providing Industry 4.0 functionality. To validate the operation of the platform, case studies were made with a typical robotic arm in an automated industry and with an automated didactic plant. As a result, the impact of the CPPS Retrofitting is shown