A Bayesian network based framework to evaluate reliability in wind turbines

  • Ashrafi, Maryam (Industrial Engineering and Management Systems Department, Amirkabir University of Technology) ;
  • Davoudpour, Hamid (Industrial Engineering and Management Systems Department, Amirkabir University of Technology) ;
  • Khodakarami, Vahid (Industrial Engineering Department, Bu-Ali Sina University)
  • Received : 2015.06.22
  • Accepted : 2016.03.20
  • Published : 2016.05.25


The growing complexity of modern technological systems requires more flexible and powerful reliability analysis tools. Existing tools encounter a number of limitations including lack of modeling power to address components interactions for complex systems and lack of flexibility in handling component failure distribution. We propose a reliability modeling framework based on the Bayesian network (BN). It can combine historical data with expert judgment to treat data scarcity. The proposed methodology is applied to wind turbines reliability analysis. The observed result shows that a BN based reliability modeling is a powerful potential solution to modeling and analyzing various kinds of system components behaviors and interactions. Moreover, BN provides performing several inference approaches such as smoothing, filtering, what-if analysis, and sensitivity analysis for considering system.


  1. Arabian-Hoseynabadi, H., Tavner, P.J. and Oraee, H. (2010), "Reliability comparison of direct-drive and geared drive wind turbine concepts", Wind Energy, 13(1), 62-73.
  2. AgenaRisk. (2014), Retrieved 2012, from AgenaRisk:
  3. Almond, R. (1992), An extended example for testing graphical belief. Technical Report 6, Statistical Sciences Inc.
  4. Barlow, R. (1988), Using influence diagrams. In C. Clarotti, & D. Lindley, Accelerated life testing and experts' opinions in reliability.
  5. Bobbio, A., Portinale, L., Minichino, M. and Ciancamerla, E. (2001), "Improving the analysis of dependable systems by mapping fault trees into Bayesian networks", Reliab. Eng. Syst. Saf., 71(3), 249-260.
  6. Caspeele, R. and Taerwe, L. (2013), "Numerical Bayesian updating of prior distributions for concrete strength properties considering conformity control", Adv. Concrete Constr., 1(1), 85-102.
  7. Cheng, J. and Li, Q. (2009), "Reliability based analysis of torsional divergence of long span suspension bridges", Wind Struct., 12(2), 121-132.
  8. Global Wind Energy Council (GWEC). (2012), Global Wind Energy Outlook 2012, Global Wind Energy Council (GWEC).
  9. Guo, H., Watson, S., Tavner, P. and Xiang, J. (2009), "Reliability analysis for wind turbines with incomplete failure data collected from after the date of initial installation", Reliab. Eng. Syst. Saf., 94(6), 1057-1063.
  10. Lam, H.F. and Yang, J. (2015), "Bayesian structural damage detection of steel towers using measured modal parameters", Eartq. Struct.
  11. Langseth, H. and Portinale, L. (2007), "Bayesian networks in reliability", Reliab. Eng. Syst. Saf., 92, 92-108.
  12. McMillan, D. and Ault, G. (2007), "Quantification of condition monitoring benefit for offshore wind turbines", Wind Energy, 31(4), 267-285.
  13. Pearl, J. (1988), Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann Publishers.
  14. Portinale, L. and Bobbio, A. (1999), "Bayesian networks for dependability analysis: an application to digital control reliability", Proceedings of the 15th conference on uncertainty in artificial intelligence, San Francisco, CA: Morgan Kaufmann Publishers.
  15. Ribrant, J. (2006), Reliability performance and maintenance, A survey of failures in windpower systems, KTH School of Electrical Engineering, Master Thesis.
  16. Saeed, A. (2008), Online Condition Monitoring System for Wind Turbine Case Study, Blekinge Institute of Technology.
  17. Solano-Soto, J. and Sucar, L. (2001), "A methodology for reliable system design", Proceedings of the 4th international conference on industrial andengineering applications of artificial intelligence and expert systems, Berlin, Germany: Lecture notes in artificial intelligence, vol. 2070. Springer.
  18. Sterzinge, G. (2004), Wind turbine development, Renewable Energy Policy Project (REPP),Washington.
  19. Tavner, P., Spinato, F., Van Bussel, G. and Koutoulakos, E. (2008), "The reliability of different wind turbine concepts, with relevance to offshore applications", European Wind Energy Conference, Brussels.
  20. Torres-Toledano, J. and Sucar, L. (1998), "Bayesian networks for reliability analysis of complex systems", Proceedings of the 6th Ibero-American conference on AI (IBERAMIA 98), Berlin, Germany: Lecture notes in artificial intelligence, vol. 1484. Springer.
  21. Walford, C. (2006), Wind turbine reliability: Understanding and minimizing wind turbine operation and maintenance costs, Albuquerque, New Mexico 87185 and Livermore, California 94550: Sandia National Laboratories, Tech. Rep. SAND2006-1100.
  22. Zhang, L.L. and Li, J. (2007), "Probability density evolution analysis on dynamic response and reliability estimation of wind-excited transmission towers", Wind Struct., 10(1), 45-60.