Abstract
Structural maintenance operations in wind energy sector are steering towards condition based maintenance (CBM) which requires prognostic estimates of existing condition of the wind turbine (WT) structural systems that is damage propagation and remaining useful life (RUL). WT blades are highly vulnerable structural components that are subjected to continuous cyclic loads of wind and self weight variation. A method for estimation of RUL of wind turbine blades considering the fatigue mode of failure is proposed in this paper. Stochastic life expectancy methods that use Bayesian updating with measurements of evolving damage for damage propagation estimation have proven to be reliable in RUL estimation. In this study probability density functions for the RUL of WT blades are estimated for diffident initial crack sizes and particle filtering method is used for forecasting the evolution of fatigue damage addressing the non-linearity and uncertainty in crack propagation. The stresses on a numerically modeled life size onshore WT blade subjected to turbulence are used in computing the crack propagation observation data for particle filters.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wymore, M.L., Van Dam, J.E., Ceylan, H., Qiao, D.: A survey of health monitoring systems for wind turbines. Renew. Sust. Energ. Rev. 52(1069283), 976–990 (2015)
Griffin, D.A., Malkin, M.C.: I Introduction, Principal Engineer, Turbine Engineering Group, Senior Engineer, Press Release, Suzlon Energy Unlimited.: Lessons learned from recent blade failures: primary causes and risk-reducing technologies. In: 49th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, Jan 2011, pp. 1–9
El-Thalji, I., Jantunen, E.: On the development of condition based maintenance strategy for offshore wind farm: requirement elicitation process. Energy Proc. 24, 328–339 (2012)
Gupta, A., Lawsirirat, C.: Strategically optimum maintenance of monitoring-enabled multi-component systems using continuous-time jump deterioration models. J. Qual. Maint. Eng. 12(3), 306–329 (2006)
Campos, J.: Development in the application of ICT in condition monitoring and maintenance. Comput. Ind. 60(1), 1–20 (2009)
Ciang, C.C., Lee, J.-R., Bang, H.-J.: Structural health monitoring for a wind turbine system: a review of damage detection methods. Meas. Sci. Technol. 19(12), 122001 (2008)
Florian, M., Sørensen, J.: Wind turbine blade life-time assessment model for preventive planning of operation and maintenance. J. Mar. Sci. Eng. 3(3), 1027–1040 (2015)
Al-Khudairi, O., Hadavinia, H., Little, C., Gillmore, G., Greaves, P., Dyer, K.: Full-scale fatigue testing of a wind turbine blade in flapwise direction and examining the effect of crack propagation on the blade performance. Materials 10(10), 1152 (2017)
Yang, W., Peng, Z., Wei, K., Tian, W.: Structural health monitoring of composite wind turbine blades: challenges, issues and potential solutions. IET Renew. Power Gener. 11(4), 411–416 (2017)
Mcgugan, M., Pereira, G., Sørensen, B.F., Toftegaard, H., Branner, K.: Damage tolerance and structural monitoring for wind turbine blades. Philos. Trans. R. Soc. A 373(2035) (2015)
Nielsen, J.S., Sørensen, J.D.: Bayesian estimation of remaining useful life for wind turbine blades. Energies 10(5), 664 (2017)
Kong, C., Kim, T., Han, D., Sugiyama, Y.: Investigation of fatigue life for a medium scale composite wind turbine blade. Int. J. Fatigue 28(10), 1382–1388 (2006)
Schulz, M.J., Sundaresan, M.J.: Smart sensor system for structural condition monitoring of wind turbines: 30 May 2002–30 Apr 2006, subcontract report NREL/SR-500-40089. Technical report, National Renewable Energy Laboratory (2002)
Shokrieh, M.M., Rafiee, R.: Simulation of fatigue failure in a full composite wind turbine blade. Compos. Struct. 74(3), 332–342 (2006)
Lading, L., McGugan, M., Sendrup, P., Rheinländer, J., Rusborg, J.: Fundamentals for remote structural health monitoring of wind turbine blades – a preproject annex B – sensors and non-destructive testing methods for damage detection in wind turbine blades fundamentals for remote structural health monitoring of wind turbine. Technical report, Forskningscenter Risoe, Risoe-R (2002)
Yao, R., Pakzad, S.N.: Auto-regressive statistical pattern recognition algorithms for damage detection in civil structures. Mech. Syst. Signal Process. 31, 355–368 (2012)
Shahidi, S.G., Yao, R., Chamberlain, M.B.W., Nigro, M.B., Thorsen, A., Pakzad, S.N.: Data-driven structural damage identification using DIT. In: Imac33 pp. 2–9 (2015)
Yao, R., Pakzad, S.N., Venkitasubramaniam, P.: Compressive sensing based structural damage detection and localization using theoretical and metaheuristic statistics. Struct. Control. Health Monit. 24(4) (2017)
Kirikera, G.R., Schulz, M.J., Sundaresan, M.J.: Multiple damage identification on a wind turbine blade using a structural neural system. In: Sensor Systems and Networks: Phenomena, Technology, and Applications for NDE and Health Monitoring. Proceedings of the SPIE, vol. 6530, p. T5300 (2007)
Rumsey, M.A., Paquette, J.A.: Structural health monitoring of wind turbine blades. In: Smart Sensor Phenomena, Technology, Networks, and Systems 2008, vol. 6933, p. 69330E. International Society for Optics and Photonics (2008)
Zhang, F., Li, Y., Yang, Z., Zhang, L.: Investigation of wind turbine blade monitoring based on optical fiber Brillouin sensor. In: 2009 International Conference on Sustainable Power Generation and Supply, pp. 1–4 (2009)
Kim, S., Adams, D.E., Sohn, H., Rodriguez-Rivera, G., Myrent, N., Bond, R., Vitek, J., Carr, S., Grama, A., Meyer, J.J.: Crack detection technique for operating wind turbine blades using vibro-acoustic modulation. Struct. Health Monit. 13(6), 660–670 (2014)
LeBlanc, B., Niezrecki, C., Avitabile, P., Chen, J., Sherwood, J.: Damage detection and full surface characterization of a wind turbine blade using three-dimensional digital image correlation. Struct. Health Monit. 12, 430–439 (2013)
Dutton, A.G., Blanch, M.J., Vionis, P., Lekou, D., van Delft, D.R.V., Joosse, D., Anastassopoulos, P.A., Kouroussis, A., Kossivas, T., Philippidis, T.T., Assimakopoulou, T.P.: Acoustic emission condition monitoring of wind turbine rotor blades: laboratory certification testing to large scale in service deployment. In: European Wind Energy Conference, pp. 1–11 (2003)
Kirikera, G.R., Shinde, V., Schulz, M.J., Sundaresan, M.J., Hughes, S., van Dam, J., Nkrumah, F., Grandhi, G., Ghoshal, A.: Monitoring multi-site damage growth during quasi-static testing of a wind turbine blade using a structural neural system. Struct. Health Monit. Int. J. 7(2), 157–173 (2008)
Ou, Y., Dertimanis, V.K., Chatzi, E.N.: Experimental damage detection of a wind turbine blade under varying operational conditions. In: Proceedings of the ISMA, pp. 4183–4198 (2016)
Ou, Y., Chatzi, E.N., Dertimanis, V.K., Spiridonakos, M.D.: Vibration-based experimental damage detection of a small-scale wind turbine blade. Struct. Health Monit. 16(1), 79–96 (2016)
Si, X.S., Wang, W., Hu, C.H., Zhou, D.H.: Remaining useful life estimation – a review on the statistical data driven approaches. Eur. J. Oper. Res. 213(1), 1–14 (2011)
Vachtsevanos, G., Lewis, F.L., Roemer, M, Hess, A., Wu, B.: Intelligent Fault Diagnosis and Prognosis for Engineering Systems. Wiley, Hoboken (2006)
Benedetti, M., Fontanari, V., Battisti, L.: Structural health monitoring of wind towers: residual fatigue life estimation. Smart Mater. Struct. 22(4), 045017 (2013)
Sikorska, J.Z., Hodkiewicz, M., Ma, L.: Prognostic modelling options for remaining useful life estimation by industry. Mech. Syst. Signal Process. 25(5), 1803–1836 (2011)
Degrieck, J., Van Paepegem, W.: Fatigue damage modelling of fibre-reinforced composite materials: review. Appl. Mech. Rev. 54(4), 279–300 (2001)
Beganovic, N., Njiri, J.G., Rothe, S., Soffker, D.: Application of diagnosis and prognosis to wind turbine system based on fatigue load. In: 2015 IEEE Conference on Prognostics and Health Management (PHM), pp. 1–6. IEEE (2015)
Hayat, K., Asif, M., Ali, H.T., Ijaz, H., Mustafa, G.: Fatigue life estimation of large-scale composite wind turbine blades. In: 2015 12th International Bhurban Conference on Applied Sciences and Technology (IBCAST), pp. 60–66 (2015)
Sanchez, H., Sankararaman, S., Escobet, T., Puig, V., Frost, S., Goebel, K.: Analysis of two modeling approaches for fatigue estimation and remaining useful life predictions of wind turbine blades. In: Third European PHM Conference (2016)
Bartram, G., Mahadevan, S.: Dynamic Bayesian networks for prognosis. In: PHM 2013 – Proceedings of the Annual Conference of the Prognostics and Health Management Society 2013, pp. 167–184 (2013)
Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking. IEEE Trans. Signal Process. 50(2), 174–188 (2002)
Cadini, F., Zio, E., Avram, D.: Monte Carlo-based filtering for fatigue crack growth estimation. Probab. Eng. Mech. 24(3), 367–373 (2009)
Zio, E., Peloni, G.: Particle filtering prognostic estimation of the remaining useful life of nonlinear components. Reliab. Eng. Syst. Saf. 96(3), 403–409 (2011)
Haile, M.A., Riddick, J.C., Assefa, A.H.: Robust particle filters for fatigue crack growth estimation in rotorcraft structures. IEEE Trans. Reliab. 65(3), 1438–1448 (2016)
Orchard, M., Wu, B., Vachtsevanos, G.: A particle filtering frmaework for failure prognosis. In: World Tribology Congress III, vol. 2, pp. 1–2, Washington, DC (2005)
An, D., Choi, J.H., Kim, N.H.: Prognostics 101: A tutorial for particle filter-based prognostics algorithm using Matlab. Reliab. Eng. Syst. Saf. 115:161–169 (2013)
Orchard, M.E., Vachtsevanos, G.J.: A particle-filtering approach for on-line fault diagnosis and failure prognosis. Trans. Inst. Meas. Control. 31(3–4), 221–246 (2009)
Jonkman, J., Butterfield, S., Musial, W., Scott, G.: Definition of a 5-MW reference wind turbine for offshore system development. Technical report, National Renewable Energy Limited, Golden (2009)
Tezduyar, T.E., Sathe, S., Schwaab, M., Conklin, B.S.: Arterial fluid mechanics modeling with the stabilized space – time fluid – structure interaction technique. Int. J. Numer. Methods Fluids 2007, 601–629 (2008)
Jonkma, J., Jonkman, B.: FAST, National Renewable Energy Laboratory. NREL (2016)
Stephens, R.I., Fatemi, A., Stephens, R.R., Fuchs, H.O.: Metal Fatigue in Engineering, 2nd edn. Wiley, New York (2001)
Downing, S.D., Socie, D.F.: Simple rainflow counting algorithms. Int. J. Fatigue 4(1), 31–40 (1982)
Acknowledgements
Research funding is partially provided by the National Science Foundation through Grant No. CMMI-1351537 by Hazard Mitigation and Structural Engineering program, and by a grant from the Commonwealth of Pennsylvania, Department of Community and Economic Development, through the Pennsylvania Infrastructure Technology Alliance (PITA).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 The Society for Experimental Mechanics, Inc.
About this paper
Cite this paper
Valeti, B., Pakzad, S.N. (2019). Estimation of Remaining Useful Life of a Fatigue Damaged Wind Turbine Blade with Particle Filters. In: Pakzad, S. (eds) Dynamics of Civil Structures, Volume 2. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-319-74421-6_42
Download citation
DOI: https://doi.org/10.1007/978-3-319-74421-6_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-74420-9
Online ISBN: 978-3-319-74421-6
eBook Packages: EngineeringEngineering (R0)