Models for lifetime estimation: an overview with focus on applications to wind turbines
Thomas M. Welte , Kesheng Wang
Advances in Manufacturing ›› 2014, Vol. 2 ›› Issue (1) : 79 -87.
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.
Lifetime estimation / Model classification / Wind turbine
| [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] |
|
| [3] |
|
| [4] |
|
| [5] |
|
| [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] |
|
| [8] |
Rausand M, Høyland A (2004) System reliability theory: models, statistical methods, and applications, 2nd edn. Wiley, Hoboken |
| [9] |
|
| [10] |
|
| [11] |
|
| [12] |
Ascher H, Feingold H (1984) Repairable systems reliability: modeling, inference, misconceptions and their causes. CRC Press/Marcel Dekker, New York |
| [13] |
|
| [14] |
|
| [15] |
|
| [16] |
|
| [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] |
|
| [19] |
|
| [20] |
|
| [21] |
|
| [22] |
|
| [23] |
|
| [24] |
|
| [25] |
|
| [26] |
|
| [27] |
|
| [28] |
|
| [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] |
|
| [33] |
|
| [34] |
|
| [35] |
|
| [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] |
|
| [38] |
|
| [39] |
|
| [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] |
|
| [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] |
|
| [45] |
|
| [46] |
Hameed Z, Vatn J (2012) State based models applied to offshore wind turbine maintenance and renewal. In: Berenguer C, Grall A, Soares CG (eds) Advances in safety, reliability and risk management, proceedings of the European safety and reliability conference, ESREL 2011. CRC Press/Balkema, Leiden, pp 989–996 |
| [47] |
|
| [48] |
|
| [49] |
|
| [50] |
|
| [51] |
|
| [52] |
Mesquita Brandão 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 |
/
| 〈 |
|
〉 |