Models for lifetime estimation: an overview with focus on applications to wind turbines

  • Thomas M. Welte Kesheng Wang
Expand
  • 1. SINTEF Energy Research, 7465 Trondheim, Norway
    2. Department of Production and Quality Engineering, Norwegian
    University of Science and Technology, 7491 Trondheim,
    Norway

Received date: 2014-01-16

  Online published: 2014-02-14

Abstract

This paper provides an overview of models and methods for estimation of lifetime of technical components. Although the focus in this paper is on wind turbine applications, the major content of the paper is of general nature. Thus, most of the paper content is also valid for lifetime models applied to other technical systems. The models presented and discussed in this paper are classified in different types of model classes. The main classification used in this paper divides the models in the following classes: physical models, stochastic models, data-driven models and artificial intelligence, and combined models. The paper provides an overview of different models for the different classes. Furthermore, advantages and disadvantages of the models are discussed, and the estimation of model parameters is briefly described. Finally, a number of literature examples are given in this paper, providing an overview of applications of different models on wind turbines.

Cite this article

Thomas M. Welte Kesheng Wang . Models for lifetime estimation: an overview with focus on applications to wind turbines[J]. Advances in Manufacturing, 2014 , 2(1) : 79 -87 . DOI: 10.1007/s40436-014-0064-3

References

1. Loucks DP, van Beek E (2005) Water resources systems planning

and management: an introduction to methods, models and

applications, Chap. 6 ‘‘Data-based models’’. United Nations

Educational, Scientific and Cultural Organization, Paris

2. Poole DL, Mackworth AK (2010) Artificial intelligence: foundations

of computational agents. Cambridge University Press,

Cambridge

3. Meeker WQ, Escobar LA (1998) Statistical methods for reliability

data. Wiley, New York

4. Cooke RM (1992) Experts in uncertainty: opinion and subjective

probability in science. Oxford University press, New York

5. Meyer MA, Booker JM (2001) Eliciting and analyzing expert

judgment: a practical guide. Academic Press, London

6. Dragomir OE, Gouriveau R, Zerhouni N et al (2007) Framework

for a distributed and hybrid prognostic system. In: The 4th IFAC

conference on management and control of production and

logistics

7. Dowling NE (1999) Mechanical behavior of materials: engineering

methods for deformation, fracture and fatigue. Prentice

Hall, Upper Saddle River

8. Rausand M, Høyland A (2004) System reliability theory: models,

statistical methods, and applications, 2nd edn. Wiley, Hoboken

9. Ross SM (1996) Stochastic processes, 2nd edn. Wiley, New York

10. Lindqvist BH, Elvebakk G, Heggland K (2003) The trendrenewal

process for statistical analysis of repairable systems.

Technometrics 45:31–44

11. Aalen OO, Borgan Ø, Gjessing HK (2008) Survival and event

history analysis: a process point of view. Springer, New York

12. Ascher H, Feingold H (1984) Repairable systems reliability:

modeling, inference, misconceptions and their causes. CRC

Press/Marcel Dekker, New York
13. van Noortwijk JM (2009) A survey of the application of gamma

processes in maintenance. Reliab Eng Syst Saf 94:2–21
14. Jensen FV (2001) Bayesian networks and decision graphs.

Springer, New York

15. Engelbrecht AP (2007) Computational intelligence: an introduction,

vol 2. Wiley, Chichester

16. Wang K (2005) Applied computational intelligence in intelligent

manufacturing systems. Advanced Knowledge International,

Adelaide

17. Peel L (2008) Data driven prognostics using a Kalman filter

ensemble of neural network models. In: International conference

on prognostics and health management

18. Barlett EB, Uhrig RE (1992) Nuclear power plant status diagnostics

using an artificial neural network. Nucl Technol

97:272–281

19. Santosh TV, Srivastava A, Sanyasi Rao VVS et al (2009) Diagnostic

system for identification of accident scenarios in nuclear

power plants using artificial neural networks. Reliab Eng Syst Saf

94:759–762

20. Campolucci P, Uncini A, Piazza F et al (1999) On-line learning

algorithms of locally recurrent neural networks. IEEE Trans

Neural Netw 10:253–271

21. Zadeh L (1965) Fuzzy sets. Inform Control 8:338–353

22. Yan J, Koc¸ M, Lee J (2004) A prognostic algorithm for machine

performance assessment and its application. Prod Plan Control

15:796–801

23. Zio E, Maio FD (2010) A data-driven fuzzy approach for predicting

the remaining useful life in dynamic failure scenarios of a

nuclear system. Reliab Eng Syst Saf 95:49–57

24. Kolodner JL (1992) An introduction to case-based reasoning.

Artif Intell Rev 6:3–34

25. Kolodner JL (1993) Case-based reasoning. Morgan Kaufmann,

San Mateo

26. Nilsson NJ (1998) Artificial intelligence: a new synthesis. Morgan

Kaufmann, San Francisco

27. O’Connor PDT, Newton D, Bromley R (2002) Practical reliability

engineering, 4th edn. Wiley, Chichester

28. Wang WQ, Goldnaraghi MF, Ismail F (2004) Prognosis of

machine health condition using neuro-fuzzy systems. Mech Syst

Signal Process 18:813–831

29. Sutherland HJ (1999) On the fatigue analysis of wind turbines.

Report No. SAND99-0089. Sandia National Laboratories,

Albuquerque

30. Nijssen RPL (2007) Fatigue life prediction and strength degradation

of wind turbine rotor blade composites. Dissertation, Delft

University

31. Nielsen JJ, Sørensen JD (2010) Bayesian networks as a decision

tool for O&M of offshore wind turbines. In: Proceedings of the

5th international ASRANet conference

32. Nielsen JJ, Sørensen JD (2011) On risk-based operation and

maintenance of offshore wind turbine components. Reliab Eng

Syst Saf 96:218–229

33. Gray CS, Watson SJ (2009) Physics of failure approach to wind

turbine condition based maintenance. Wind Energy 13:395–405

34. Marin JC, Barroso A, Paris F et al (2007) Study of damage and

repair of blades of a 300 kW wind turbine. Energy 33:1068–1083

35. Ronold KO, Wedel-Heinen J, Christensen CJ (1999) Reliabilitybased

fatigue design of wind-turbine rotor blades. Eng Struct

21:1101–1114

36. Sutherland HJ, Mandell JF (1996) Application of the U.S. high

cycle fatigue data base to wind turbine blade lifetime predictions.

In: Proceedings of energy week 1996. ASME (American Society

of Mechanical Engineers), Calgary

37. Guo H, Watson S, Tavner P et al (2009) Reliability analysis for

wind turbines with incomplete failure data collected from after

the date of initial installation. Reliab Eng Syst Saf 94:1057–1063

38. Andrawus JA, Watson J, Kishk M (2007) Modelling system failures

to optimise wind turbine maintenance. Wind Eng 31:503–522
39. Spinato F, Tavner PJ, van Bussel GJW et al (2008) Reliability of

wind turbine subassemblies. IET Renew Power Gener 3:387–401

40. Tavner PJ, van Bussel GJW, Spinato F (2006) Machine and

converter reliabilities in wind turbines. In: Proceedings of the IET

3rd international conference on power electronics, machine and

drives, 4–6 April 2006, Dublin

41. Rademakers LWMM, Braam H, Verbruggen TW (2003) R&D

needs for O&M of wind turbines. In: European wind energy

conference 2003, 16–19 June 2003, Madrid

42. Coolen FPA, Spinato F, Venkat D (2008) On modelling of

grouped reliability data for wind turbines. IMA J Manag Math

21:363–372

43. Tavner PJ, Ciang J, Spinato F (2005) Improving the reliability of

wind turbine generation and its impact on overall distribution

network reliability. In: Proceedings of the 18th conference on

electricity distribution (CIRED 2005), 6–9 June 2005, Torino

44. Tavner P, Edwards C, Brinkman A et al (2006) Influence of wind

speed on wind turbine reliability. Wind Eng 30:55–72

45. Tavner PJ, Xiang J, Spinato F (2007) Reliability analysis for wind

turbines. Wind Energy 10:1–18

46. Hameed Z, Vatn J (2012) State based models applied to offshore

wind turbine maintenance and renewal. In: Berenguer C, Grall A,

SoaresCG(eds)Advances in safety, reliability and riskmanagement,

proceedings of the European safety and reliability conference, ESREL

2011. CRC Press/Balkema, Leiden, pp 989–996

47. Besnard F, Bertling L (2010) An approach for condition-based

maintenance optimization applied to wind turbine blades. IEEE

Trans Sustain Energy 1:77–83

48. Byon E, Ding Y (2010) Season-dependent condition-based

maintenance for a wind turbine using a partially observed Markov

decision process. IEEE Trans Power Syst 25:1823–1834

49. Garcia MC, Sanz-Bobi MA, del Pico J (2006) SIMAP: intelligent

system for predictive maintenance application to the health

condition of a wind turbine gearbox. Comput Ind 57:552–568

50. Kusiak A, Verma A (2012) Analyzing bearing faults in wind

turbines: a data-mining approach. Renew Energy 48:110–116

51. Kusiak A, Li W (2011) The prediction and diagnosis of wind

turbine faults. Renew Energy 36:16–23

52. Mesquita Branda˜o RF, Beleza Carvalho JA, Maciel Barbosa FP

(2012) Forecast of faults in a wind turbine gearbox. In: Elektro

2012, pp 170–173

53. Kim K, Parthasarathy G, Uluyol O et al (2011) Use of SCADA

data for failure detection in wind turbines. In: Energy sustainability

conference and fuel cell conference, 7–10 August 2011,

Washington, DC
Outlines

/