Friction estimation in wind turbine blade bearings

Summary The influence of friction on wind turbines (WTs) is often seen as undesirable as it has been proven to be the root cause of different severe damages, where the most vulnerable WT's components are bearings. Accurate modelling and identification of friction affects are thus required in order to characterise frictional behaviour and prevent severe damages. This paper addresses the problem on the real-time estimation of friction levels that vary in time with different rates in the WT's blade bearings. Because of ageing or damage progression in bearings, friction parameters change in time. Knowing the material properties and a damage model, monitoring of the friction can be used for condition monitoring of WT's blade bearings. The considered nonlinear identification methods suitable for estimation of fast varying parameters are the extended Kalman filter (EKF), the unscented Kalman filter, the particle filter (PF) and the PF combined with the EKF. This paper also proposes a novel modification of a differential evolution algorithm for the identification of parameters that slowly vary in time. The main contribution of this paper is the algorithm, which combines the modified differential evolution algorithm with optimal nonlinear filters for the estimation of parameters which vary at different rates. A LuGre friction model and a realistic WT sensor setup are selected. The techniques are benchmarked against simulated WT data, and their performances are compared and discussed. Copyright © 2015 John Wiley & Sons, Ltd.

[1]  J. Bellantoni,et al.  A square root formulation of the Kalman- Schmidt filter. , 1967 .

[2]  Demosthenis D. Rizos,et al.  Identification of pre-sliding friction dynamics. , 2004, Chaos.

[3]  Ali Charara,et al.  Experimental evaluation of observers for tire–road forces, sideslip angle and wheel cornering stiffness , 2008 .

[4]  Laura R. Ray,et al.  Nonlinear Tire Force Estimation and Road Friction Identification: Simulation and Experiments, , 1997, Autom..

[5]  Poul Henning Kirkegaard,et al.  Blade Bearing Friction Estimation of Operating Wind Turbines , 2012 .

[6]  Qi Cheng,et al.  A new nonlinear observer using unscented Kalman filter to estimate sideslip angle, lateral tire road forces and tire road friction coefficient , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[7]  E. J. Wiggelinkhuizen,et al.  Assessment of Condition Monitoring Techniques for Offshore Wind Farms , 2008 .

[8]  Branko Ristic,et al.  Bearings-Only Tracking of Manoeuvring Targets Using Particle Filters , 2004, EURASIP J. Adv. Signal Process..

[9]  Keith Worden,et al.  Generalised NARX shunting neural network modelling of friction , 2007 .

[10]  Konrad Reif,et al.  Using the Unscented Kalman Filter and a Non-linear Two-track Model for Vehicle State Estimation , 2008 .

[11]  Kyongsu Yi,et al.  Tire-Road Friction-Coefficient Estimation , 2010, IEEE Control Systems.

[12]  E. Piras,et al.  XPS analysis on the influence of water on the evolution of zinc dialkyldithiophosphate–derived reaction layer in lubricated rolling contacts , 2012 .

[13]  J. Beck,et al.  Bayesian State and Parameter Estimation of Uncertain Dynamical Systems , 2006 .

[14]  John A. Grogg,et al.  Algorithms for Real-Time Estimation of Individual Wheel Tire-Road Friction Coefficients , 2006, IEEE/ASME Transactions on Mechatronics.

[15]  Rudolph van der Merwe,et al.  The Unscented Kalman Filter , 2002 .

[16]  H. Muro,et al.  Microstructural, microhardness and residual stress changes due to rolling contact , 1970 .

[17]  P. Lugt,et al.  The Composition of Reaction Layers on Rolling Bearings Lubricated with Gear Oils and Its Correlation with Rolling Bearing Performance , 2012 .

[18]  Jan Swevers,et al.  An integrated friction model structure with improved presliding behavior for accurate friction compensation , 1998, IEEE Trans. Autom. Control..

[19]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[20]  Rajesh Rajamani,et al.  GPS-based real-time identification of tire-road friction coefficient , 2002, IEEE Trans. Control. Syst. Technol..

[21]  G. Morales-Espejel,et al.  Micropitting Modelling in Rolling–Sliding Contacts: Application to Rolling Bearings , 2011 .

[22]  Andrew E. Yagle,et al.  Nonlinear system identification of hydraulic actuator. Friction dynamics using a Hammerstein model , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[23]  G. Kitagawa.  Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models , 1996 .

[24]  Maarten Steinbuch,et al.  Frequency domain identification of dynamic friction model parameters , 2002, IEEE Trans. Control. Syst. Technol..

[25]  Jan Swevers,et al.  Modification of the Leuven integrated friction model structure , 2002, IEEE Trans. Autom. Control..

[26]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[27]  Branko Ristic,et al.  Beyond the Kalman Filter: Particle Filters for Tracking Applications , 2004 .

[28]  Ali Charara,et al.  Estimation of vehicle lateral tire-road forces: A comparison between extended and unscented Kalman filtering , 2009, 2009 European Control Conference (ECC).

[29]  K.J. Astrom,et al.  Dynamic Friction Models and Control Design , 1993, 1993 American Control Conference.

[30]  Carlos Canudas de Wit,et al.  A new model for control of systems with friction , 1995, IEEE Trans. Autom. Control..

[31]  T. A. Harris,et al.  Fatigue Failure and Ball Bearing Friction , 2000 .

[32]  E. Ioannides,et al.  Fluid-Crack Interaction in Lubricated Rolling-Sliding Contact , 2008 .

[33]  Thomas Parisini,et al.  Fault Detection in Mechanical Systems With Friction Phenomena: An Online Neural Approximation Approach , 2007, IEEE Transactions on Neural Networks.

[34]  Yongqian Liu,et al.  Smart Monitoring of Wind Turbines Using Neural Networks , 2009 .

[35]  J. Kim.  Identification of lateral tyre force dynamics using an extended Kalman filter from experimental road test data , 2009 .

[36]  Tegoeh Tjahjowidodo,et al.  Identification of pre-sliding and sliding friction dynamics: Grey box and black-box models , 2007 .

[37]  Edoardo Sabbioni,et al.  A methodology for vehicle sideslip angle identification: comparison with experimental data , 2007 .

[38]  Poul Henning Kirkegaard,et al.  Identification of time-varying nonlinear systems using differential evolution algorithm , 2014 .

[39]  Laura Ryan Ray,et al.  Nonlinear state and tire force estimation for advanced vehicle control , 1995, IEEE Trans. Control. Syst. Technol..