Uma abordagem evolutiva com filtro de partículas e sistemas nebulosos para o monitoramento da condição de equipamentos
Resumo
Este artigo propõe uma abordagem adaptativa para representar modelos de degradação em equipamentos, combinando filtro de partículas e sistemas nebulosos. Experimentos com a base de dados da NASA Ames Prognostics Center of Excellence (PCoE), que disponibiliza dados históricos de baterias de íons de lítio, mostrou que o método adaptativo apresenta melhorias na estimativa em tempo real da condição dos equipamentos em comparação com abordagens tradicionais.
Referências
Camargos, M., Bessa, I., Junior, L. A. Q. C., Coutinho, P., Leite, D. F., and Palhares, R. M. (2021). Evolving fuzzy system applied to battery charge capacity prediction for fault prognostics. In Joint Proceedings of the 19th World Congress of the International Fuzzy Systems Association (IFSA), the 12th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), and the 11th International Summer School on Aggregation Operators (AGOP).
Chen, C., Vachtsevanos, G., and Orchard, M. E. (2012). Machine remaining useful life prediction: An integrated adaptive neuro-fuzzy and high-order particle filtering approach. Mechanical Systems and Signal Processing, 28:597–607.
Cosme, L., D’Angelo, M., Caminhas, W., Yin, S., and Palhares, R. (2018). A novel fault prognostic approach based on particle filters and differential evolution. Applied Intelligence, 48:834–853.
Jang, J. S., Sun, C. T., and Mizutani, E. (1997). Neuro-fuzzy And Soft Computing: A Computational Approach To Learning And Machine Intelligence. Prentice-Hall, 1th edition.
Jouin, M., Gouriveau, R., Hissel, D., Péra, M.-C., and Zerhouni, N. (2016). Particle filter-based prognostics: Review, discussion and perspectives. Mechanical Systems and Signal Processing, 72-73:2–31.
Komijani, M., Lucas, C., Araabi, B., and Kalhor, A. (2012). Introducing evolving takagi-sugeno method based on local least squares support vector machine models. Evolving Systems, (3):81–93.
Saha, B. and Goebel, K. (2011). Model adaptation for prognostics in a particle filtering framework. International Journal of Prognostics and Health Management, page 61.
Sikorska, J., Hodkiewicz, M., and Ma, L. (2011). Prognostic modelling options for remaining useful life estimation by industry. Mechanical Systems and Signal Processing, 25(5):1803–1836.
Zadeh, L. (1965). Fuzzy sets. Information and Control, 8(3):338–353.
Chen, C., Vachtsevanos, G., and Orchard, M. E. (2012). Machine remaining useful life prediction: An integrated adaptive neuro-fuzzy and high-order particle filtering approach. Mechanical Systems and Signal Processing, 28:597–607.
Cosme, L., D’Angelo, M., Caminhas, W., Yin, S., and Palhares, R. (2018). A novel fault prognostic approach based on particle filters and differential evolution. Applied Intelligence, 48:834–853.
Jang, J. S., Sun, C. T., and Mizutani, E. (1997). Neuro-fuzzy And Soft Computing: A Computational Approach To Learning And Machine Intelligence. Prentice-Hall, 1th edition.
Jouin, M., Gouriveau, R., Hissel, D., Péra, M.-C., and Zerhouni, N. (2016). Particle filter-based prognostics: Review, discussion and perspectives. Mechanical Systems and Signal Processing, 72-73:2–31.
Komijani, M., Lucas, C., Araabi, B., and Kalhor, A. (2012). Introducing evolving takagi-sugeno method based on local least squares support vector machine models. Evolving Systems, (3):81–93.
Saha, B. and Goebel, K. (2011). Model adaptation for prognostics in a particle filtering framework. International Journal of Prognostics and Health Management, page 61.
Sikorska, J., Hodkiewicz, M., and Ma, L. (2011). Prognostic modelling options for remaining useful life estimation by industry. Mechanical Systems and Signal Processing, 25(5):1803–1836.
Zadeh, L. (1965). Fuzzy sets. Information and Control, 8(3):338–353.
Publicado
21/07/2024
Como Citar
ALMEIDA, Iarah G. de; SILVA, Gabriel R. da; AGUIAR, Cauã R. C. e; CARVALHO, Gabriel S. V. de; COSME, Luciana B..
Uma abordagem evolutiva com filtro de partículas e sistemas nebulosos para o monitoramento da condição de equipamentos. In: ENCONTRO NACIONAL DE COMPUTAÇÃO DOS INSTITUTOS FEDERAIS (ENCOMPIF), 11. , 2024, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 58-65.
ISSN 2763-8766.
DOI: https://doi.org/10.5753/encompif.2024.2467.
