Bayesian based Prognostic Model for Predictive Maintenance of Offshore Wind Farms
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Abstract
The operation and maintenance costs of offshore wind farms can be significantly reduced if existing corrective actions are performed as efficient as possible and if future corrective actions are avoided by performing sufficient preventive actions. In this paper a prognostic model for degradation monitoring, fault prediction and predictive maintenance of offshore wind components is defined.
The diagnostic model defined in this paper is based on degradation, remaining useful lifetime and hybrid inspection threshold models. The defined degradation model is based on an exponential distribution with stochastic scale factor modelled by a normal distribution. Once based on failures, inspection or condition monitoring data sufficient observations on the degradation level of a component are available, using Bayes’ rule and Normal-Normal model prior exponential parameters of the degradation model can be updated. The components of the diagnostic model defined in this paper are further explained within several illustrative examples. At the end, conclusions are given and recommendations for future studies on this topic are discussed.
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Prognostic, Predictive Maintenance, Offshore Wind, O&M, Degradation Model, Remaining Useful Lifetime Model, Bayesian Updating
Asgarpour, M. & Sørensen, J.D., 2018. Bayesian based Diagnostic Model for Condition based Maintenance of Offshore Wind Farms. Energies, 11(300).
Asgarpour, M. & Sørensen, J.D., 2015. State of the Art in Operation and Maintenance Planning of Offshore Wind Farms. In European Safety & Reliability Conference (ESREL). London, pp. 1119–1125.
Gebraeel, N.Z. et al., 2005. Residual Life Distributions from Component Degradation Signals : A Bayesian Approach Residual-life distributions from component degradation. IIE Transactions, 37, pp.543–557.
Griffith, D.T. et al., 2014. Structural health and prognostics management for the enhancement of offshore wind turbine operations and maintenance strategies. Wind Energy, 17, pp.1737–1751.
Kandukuri, S.T. et al., 2016. A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management. Renewable and Sustainable Energy Reviews, 53, pp.697–708. Available at: http://dx.doi.org/10.1016/j.rser.2015.08.061.
Lau, B.C.P., Ma, E.W.M. & Pecht, M., 2012. Review of Offshore Wind Turbine Failures and Fault Prognostic Methods. In Prognostics & System Health Management Conference. Beijing: IEEE.
Li, N. et al., 2015. An Improved Exponential Model for Predicting Remaining Useful Life of Rolling Element Bearings. IEEE Transactions on Industrial Electronics, 62(12), pp.7762–7773.
Nielsen, J.S., 2013. Risk-Based Operation and Maintenance of Offshore Wind Turbines. Aalborg University.
Nielsen, J.S. & Sørensen, J.D., 2017. Bayesian Estimation of Remaining Useful Life for Wind Turbine Blades. Energ, 10(664).
Nielsen, J.S. & Sørensen, J.D., 2014. Methods for Risk- Based Planning of O&M of Wind Turbines. Energies, 7, pp.6645–6664.
Novaes, G. De et al., 2018. Prognostic techniques applied to maintenance of wind turbines: a concise and specific review. Renewable and Sustainable Energy Reviews, 81, pp.1917–1925.
Rasekhi Nejad, A. et al., 2014. A prognostic method for fault detection in wind turbine drivetrains. Engineering Failure Analysis, 42, pp.324–336. Available at: http://dx.doi.org/10.1016/j.engfailanal.2014.04.031.
Schwabacher, M. & Goebel, K., 2007. A Survey of Artificial Intelligence for Prognostics. In AIAA Fall Symposium. Arlington, Virginia: AIAA, pp. 107–114.
Si, X. et al., 2011. Remaining useful life estimation – A review on the statistical data driven approaches. European Journal of Operational Research, 213(1), pp.1–14. Available at: http://dx.doi.org/10.1016/j.ejor.2010.11.018.
Sikorska, J. & Ma, L., 2011. Prognostic modeling options for remaining useful life estimation by industry. Mechanical Systems and Signal Processing, 25, pp.1803–1836.
Sørensen, J.D., 2009. Framework for Risk-based Planning of Operation and Maintenance for Offshore Wind Turbines.
Tardieu, P., Nghiem, A. & Pineda, I., 2017. Wind energy in Europe: Scenarios for 2030, Brussels. Technical Committee CEN 319, 2010. EN 13306 Maintenance Terminology, Brussels.
Welte, T.M. & Wang, K., 2014. Models for lifetime estimation: an overview with focus on applications to wind turbines. Advances in Manufacturing, 2(1), pp.79–87.