Sistemas computacionais aplicados em Manutenção Baseado na Confiabilidade de, em especial os Mapas Cognitivos Fuzzy Dinâmicos Simplificados. / Computacionais systems Applied to Reliability-Based Maintenance of, in particular, simplified Dynamic Fuzzy Cognitive Maps.

Authors

  • Márcio Mendonça Brazilian Journals Publicações de Periódicos, São José dos Pinhais, Paraná
  • Ivan Rossato Chrun
  • Michelle Eliza Casagrande Rocha
  • Fabio Rodrigo Milanez
  • Francisco de Assis S. Junior
  • Marta Rúbia P. dos Santos
  • Carlos Alberto Paschoalino
  • Wagner Fontes Godoy
  • Marco Antonio Ferreira Finocchio

DOI:

https://doi.org/10.34117/bjdv7n8-297

Keywords:

Manutenção Centrada na Realidade, Motores Elétricos, Mapas Cognitivos Fuzzy, Análise Quantitativa.

Abstract

A indústria possui sistemas e máquinas que precisam operar dentro de parâmetros adequados para garantir a qualidade na produção (padronização, contemplação do pedido do cliente, entre outras). Além disso no atendimento dos seus clientes dentro dos tempos estabelecidos, porque o não cumprimento de prazos pode influenciar de forma significativa na perda de usuários ou na qualidade do produto, devido a necessidade de produção com tempo reduzido para fechamento de prazos. Neste contexto, é necessário manter, através da manutenção, as condições necessárias ao bom funcionamento. Assim, por meio da Manutenção Centrada na Confiabilidade com feedback quantitativo por Mapas Cognitivos Fuzzy aplicados a motores elétricos, esta pesquisa pode sugerir melhor confiabilidade. Este artigo discute a Manutenção Centrada em Confiabilidade (RCM) em relação a uma Lista de Verificação genérica de manutenção de motores elétricos. Através das ações de manutenção para correção de falhas e, ou defeitos, pode-se modelar um FCM crítico e qualitativo que apresentará um diagnóstico quantitativo aprimorado em uma proposta de ferramenta computacional para auxiliar no gerenciamento da manutenção, agregando melhorias ao sistema da empresa de um modo geral. Isso se reforça com o crescimento da indústria 4.0.

References

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PAPAGEORGIOU, E. I. (ED.). Fuzzy Cognitive Maps for Applied Sciences and Engineering. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. v. 54

TSANG, A. H. C. Strategic dimensions of maintenance management. Journal of Quality in Maintenance Engineering, v. 8, n. 1, p. 7–39, 2002.

YANG, S. K. A condition-based failure-prediction and processing-scheme for preventive maintenance. IEEE Transactions on Reliability, v. 52, n. 3, p. 373–383, set. 2003.

YESIL, E. et al. Fuzzy cognitive maps learning using Artificial Bee Colony optimization. 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). Anais...Hyderabad, India: IEEE, 2013

ACAMPORA, G.; LOIA, V. A dynamical cognitive multi-agent system for enhancing ambient intelligence scenarios. Fuzzy Systems, 2009. FUZZ-IEEE 2009. IEEE International Conference on. Anais...2009

ALGHATHBAR, K.; WIJESEKERA, D. Analyzing information flow control policies in requirements engineering. Proceedings - Fifth IEEE International Workshop on Policies for Distributed Systems and Networks, POLICY 2004, p. 193–196, 2004.

ALSYOUF, I. Maintenance practices in Swedish industries: Survey results. International Journal of Production Economics, v. 121, n. 1, p. 212–223, set. 2009.

ARRUDA, L. V. R. et al. Artificial Life Environment Modeled by Dynamic Fuzzy Cognitive Maps. IEEE Transactions on Cognitive and Developmental Systems, v. 10, n. 1, p. 88–101, 2018.

CONCEPCION, L. et al. Fuzzy-Rough Cognitive Networks: Theoretical Analysis and Simpler Models. IEEE Transactions on Cybernetics, p. 1–12, 2020.

DEHGHANIAN, P. et al. Critical Component Identification in Reliability Centered Asset Management of Power Distribution Systems Via Fuzzy AHP. IEEE Systems Journal, v. 6, n. 4, p. 593–602, 2012.

DIKOPOULOU, Z.; PAPAGEORGIOU, E. I.; VANHOOF, K. Retrieving sparser fuzzy cognitive maps directly from categorical ordinal dataset using the graphical lasso models and the MAX-threshold algorithm. IEEE International Conference on Fuzzy Systems, v. 2020- July, 2020.

FELIX, G. et al. A review on methods and software for fuzzy cognitive maps. Artificial Intelligence Review, n. March 2018, p. 1–31, 2017.

JAMSHIDI, A. et al. Dynamic risk modeling and assessing in maintenance outsourcing with FCM. Industrial Engineering and Systems Management (IESM), 2015 International Conference on. Anais...2015

JANIER, J. B.; ZAHARIA, M. F. Z. Condition Monitoring System for Induction Motor Using Fuzzy Logic Tool. 2011 First International Conference on Informatics and Computational Intelligence. Anais...IEEE, dez. 2011 Disponível em: <http://ieeexplore.ieee.org/document/6141641/>

LIANG, H.; SUN, F. Offshore mechanical maintenance based on fuzzy condition assessment and RCM analysis. 2011 International Conference on Electrical and Control Engineering. Anais...IEEE, set. 2011Disponível em: <http://ieeexplore.ieee.org/document/6058357/>

LUO, C.; WANG, H.; ZHENG, Y. Controllability of k-Valued Fuzzy Cognitive Maps. IEEE Transactions on Fuzzy Systems, v. 28, n. 8, p. 1694–1707, 2020.

MENDES, A. A.; RIBEIRO, J. L. D. Establishment of a maintenance plan based on quantitative analysis in the context of the MCC in a JIT production scenario. Producao, v. 24, n. 3, p. 675–686, 2014.

MENDONÇA, M. et al. A Subsumption Architecture to Develop Dynamic Cognitive Network-Based Models With Autonomous Navigation Application. Journal of Control, Automation and Electrical Systems, v. 24, n. 1, p. 117–128, 2013.

MENDONÇA, M. et al. Fuzzy Cognitive Maps Applied to Student Satisfaction Level in an University. IEEE Latin America Transactions, v. 13, n. 12, p. 3922–3927, 2015.

MENDONÇA, M. et al. A cooperative architecture for swarm robotic based on dynamic fuzzy cognitive maps. Engineering Applications of Artificial Intelligence, v. 59, n. May 2016, p. 122–132, 2017.

MENDONÇA, M. et al. Semi-Unknown Environments Exploration Inspired by Swarm robotics using Fuzzy Cognitive Maps. 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). Anais...New Orleans, USA: IEEE, 2019

MENDONÇA, M. et al. Multi-robot exploration using Dynamic Fuzzy Cognitive Maps and Ant Colony Optimization. 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). Anais...Glasgow, United Kingdom: IEEE, 2020a

MENDONÇA, M. et al. Multi-robot exploration using Dynamic Fuzzy Cognitive Maps and Ant Colony Optimization. IEEE International Conference on Fuzzy Systems. Anais...Glasgow, UK: IEEE, 2020b

MENDONÇA, M.; CHUN, I. R.; ROCHA, M. E. C. Dynamic fuzzy cognitive maps applied in realibility centered maintanance of electric motors. IEEE Latin America Transactions, v. 15, n. 5, p. 827–834, 2017.

MOSALLAM, A. et al. Nonlinear Relation Mining for Maintenance Prediction. 2011 Aerospace Conference. Anais...IEEE, mar. 2011 Disponível em: <http://ieeexplore.ieee.org/document/5747581/>

NÁPOLES, G. et al. On the convergence of sigmoid Fuzzy Cognitive Maps. Information Sciences, v. 349–350, 2016.

PAOLETTI, G. J.; HERMAN, G. Monitoring of electrical equipment failure indicators and zero-planned outages: Past, present and future maintenance practices. 2015 61st IEEE Pulp and Paper Industry Conference (PPIC). Anais...IEEE, jun. 2015Disponível em: <http://ieeexplore.ieee.org/document/7165712/>

PAPAGEORGIOU, E. I. (ED.). Fuzzy Cognitive Maps for Applied Sciences and Engineering. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. v. 54

TSANG, A. H. C. Strategic dimensions of maintenance management. Journal of Quality in Maintenance Engineering, v. 8, n. 1, p. 7–39, 2002.

YANG, S. K. A condition-based failure-prediction and processing-scheme for preventive maintenance. IEEE Transactions on Reliability, v. 52, n. 3, p. 373–383, set. 2003.

YESIL, E. et al. Fuzzy cognitive maps learning using Artificial Bee Colony optimization. 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). Anais...Hyderabad, India: IEEE, 2013

Published

2021-08-12

How to Cite

Mendonça, M., Chrun, I. R., Rocha, M. E. C., Milanez, F. R., S. Junior, F. de A., dos Santos, M. R. P., Paschoalino, C. A., Godoy, W. F., & Finocchio, M. A. F. (2021). Sistemas computacionais aplicados em Manutenção Baseado na Confiabilidade de, em especial os Mapas Cognitivos Fuzzy Dinâmicos Simplificados. / Computacionais systems Applied to Reliability-Based Maintenance of, in particular, simplified Dynamic Fuzzy Cognitive Maps. Brazilian Journal of Development, 7(8), 80166–80187. https://doi.org/10.34117/bjdv7n8-297

Issue

Section

Original Papers