Open Access
E3S Web Conf.
Volume 43, 2018
ASTECHNOVA 2017 International Energy Conference
Article Number 01020
Number of page(s) 12
Published online 29 June 2018
  1. K. Nouri, R. Dhaouadi, N. Benhadj Braiek, Adaptive control of a nonlinear dc motor drive using recurrent neural networks, Appl. Soft Comput. 8, 371–382 (2008) [CrossRef] [Google Scholar]
  2. A. Jain, G. Schildbach, L. Fagiano, M. Morari, On the design and tuning of linear model predictive control for wind turbines, Renew. Energy. 80, 664–673 (2015) [CrossRef] [Google Scholar]
  3. D. An, N.H. Kim, J.-H. Choi, Practical options for selecting data-driven or physics-based prognostics algorithms with reviews, Reliab. Eng. Syst. Saf. 133, 223–236 (2015) [CrossRef] [Google Scholar]
  4. F. Cadini, E. Zio, D. Avram, Monte Carlo-based filtering for fatigue crack growth estimation, Probabilistic Eng. Mech. 24, 367–373 (2009) [CrossRef] [Google Scholar]
  5. C.S. Byington, M. Watson, D. Edwards, P. Stoelting, A model-based approach to prognostics and health management for flight control actuators, 2004 IEEE Aerosp. Conf. Proc. (IEEE Cat. No.04TH8720), 6 (2004) [Google Scholar]
  6. S.M. Shin, I.S. Jeon, H.G. Kang, Surveillance test and monitoring strategy for the availability improvement of standby equipment using agedependent model, Reliab. Eng. Syst. Saf. 135, 100– 106 (2015) [CrossRef] [Google Scholar]
  7. B.M. Bole, K. Goebel, G. Vachtsevanos, Using Markov Models of Fault Growth Physics and Environmental Stresses to Optimize Control Actions, pp. 1–7 (2012) [Google Scholar]
  8. İ. Eker, Experimental on-line identification of an electromechanical system, ISA Trans. 43, 13–22 (2004) [CrossRef] [Google Scholar]
  9. I.D. Landau, Y.D. Landau, System identification and control design: using P.I.M.+ software 1990, (2015) [Google Scholar]
  10. K. Shwetha, N.S. Narahari, C.S. Prasad, Failure Mode Effect and Criticality Analysis Performance Test on Dc Brush Motors Used in Spacecraft Applications, Int. J. Res. Eng. Technol. 1, 169–176 (2013) [Google Scholar]
  11. L. Tóth, T. Tóth, Construction of a realistic signal model of transients for a ball bearing with inner race fault, Acta Polytech. Hungarica. 10, 63–80 (2013) [Google Scholar]
  12. A. Saxena, K. Goebel, D. Simon, N. Eklund, Damage propagation modeling for aircraft engine run-tofailure simulation, 2008 Int. Conf. Progn. Heal. Manag. IEEE, pp. 1–9 (2008) [Google Scholar]
  13. B. Saha, K. Goebel, Uncertainty management for diagnostics and prognostics of batteries using Bayesian techniques, IEEE Aerosp. Conf. Proc., pp. 1–8 (2008) [Google Scholar]
  14. M. Orchard, G. Kacprzynski, K. Goebel, B. Saha, G. Vachtsevanos, Advances in uncertainty representation and management for particle filtering applied to prognostics, 2008 Int. Conf. Progn. Heal. Manag. IEEE, pp. 1–6 (2008) [Google Scholar]
  15. S. Theodoridis, Machine Learning, Elsevier (2015) [Google Scholar]

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