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.
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
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