An evolutionary approach with particle filter and fuzzy systems for equipment condition monitoring

• Iarah G. de Almeida IFNMG
• Gabriel R. da Silva IFNMG
• Cauã R. C. e Aguiar IFNMG
• Gabriel S. V. de Carvalho UNIMONTES
• Luciana B. Cosme IFNMG

DOI: https://doi.org/10.5753/encompif.2024.2467

Abstract

This paper proposes an adaptive approach to representing degradation models in equipment, combining particle filter and fuzzy systems. Experiments with the NASA Ames Prognostics Center of Excellence (PCoE) database, which provides historical data on lithium-ion batteries, showed that the adaptive method improves real-time estimation of equipment condition compared to traditional approaches.

References

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.