Failure Prognosis using Dynamic Bayesian Networks and Decision Trees

  • Authors

    • Élie DJOMO Doctorant
    • Wolfgang Nzié
    • Aslain Brisco Ngnassi Djami
    2024-05-21
    https://doi.org/10.14419/s57tf607
  • Prognosis, Probability of failure, Decision trees, Dynamic Bayesian networks, System
  • Production managers commonly need to assess the reliability of production equipment throughout its life cycle. Since equipment is typically composed of multiple components, failures may stem from dependencies due to interactions. We have found that the literature does not adequately address these dependencies in the dynamic reliability models of the studied systems. The purpose of this study is to utilize decision trees (DT) and Dynamic Bayesian Networks (DBN) to approximate the failure probability of multi-component systems. We present a failure prediction method and utilize a parametric estimation approach grounded in a priori laws. The DBN learning process is assessed via a Monte-Carlo simulation method and a parametric compliance examination. Finally, this study illustrates the significant impact of using Dynamic Bayesian Networks (DBNs) in conjunction with Decision Trees (DTs) to evaluate the state of a water production system.

  • References

    1. N. N. Benjamim, L. Sara, and E. Emmanuel. problematique de l’acces ` a l’eau potable dans la ville de ngaoundéré (centre nord-cameroun). Editorial Advisory Board e, 18(2):223–230, 2005.
    2. T. Boukra. Diagnostic et pronostic des defauts des moteurs asynchrones.
    3. D. T. Bui, B. Pradhan, O. Lofman, I. Revhaug, and O. B. Dick. Landslide susceptibility assessment in the hoa binh province of vietnam: a comparison of the levenberg–marquardt and bayesian regularized neural networks. Geomorphology, 171:12–29, 2012.
    4. Y. Debbah. Developpement d’un outil de pronostic pour la maintenance des systémes mécaniques. PhD thesis, 2018.
    5. N. Ghanmi, M. A. Mahjoub, J. Khlifia, and N. E. B. Amara. Proposition d’un modèle de réseau bayésien dynamique appliqué à la reconnaissance de mots arabes manuscrits. 2012.
    6. R. Gouriveau, M. El Koujok, and N. Zerhouni. Specification d’un syst ´ eme neuro-flou de prédiction de défaillances à moyen terme. 2007.
    7. M. Kevin, Patrick. Dynamic bayesian networks: representation, inference and learning. 2002.
    8. P. Leray. Réseaux bayésiens: apprentissage et modélisation de systèmes complexes. ` habilitation à diriger les recherches, Université de Rouen, 2006.
    9. J. Li, j.S. Rebecca, G. Wang, X. Liu, Z. Li, and M. Xu. Hard drive failure prediction using Decision Trees”, Reliability Engineering and System Safety. 2017.
    10. Z. Li, T. Xu, J. Gu, Q. Dong, and L. Fu. Reliability modelling and analysis of a multi-state element based on a dynamic bayesian network. Royal Society open science, 5(4):171438, 2018.
    11. V. L. J. K. Miroslav Kvassay, Elena Zaitseva. Binary decision diagrams in reliability analysis of standard system structures. 2016.
    12. M. Nahim, Hassan. Contribution à la modélisation et à la prédiction de défaillances sur les moteurs Diesel marins. PhD thesis, 2016.
    13. P. Na¨ım, P.-H. Wuillemin, P. Leray, O. Pourret, and A. Becker. Réseaux bayésiens. Editions Eyrolles, 2011.
    14. S. E. G. C. B. A. D. T. Nefeslioglu, H.A. Assessment of landslide susceptibility by decision trees in the metropolitan area of istanbul, turkey. In Math. Probl. Eng., page 1–15. https://doi.org/10.1155/2010/901095, 2010.
    15. P. Weber and L. Jouffe. Reliability modelling with dynamic bayesian networks. 2003.
    16. I. Witten and E. Frank. Data mining practical machine learning tools and techniques. morgan kaufmann. 2005
  • Downloads

  • How to Cite

    DJOMO, Élie, Nzié, W., & Ngnassi Djami, A. B. (2024). Failure Prognosis using Dynamic Bayesian Networks and Decision Trees. International Journal of Engineering & Technology, 13(1), 143-155. https://doi.org/10.14419/s57tf607