Hybrid method for remaining useful life prediction in wind turbine systems

Abstract This paper deals with fault prognosis of wind turbine in presence of multiple faults. First, a physical model is presented and used for structural analysis, sensor placement, and clusters generation characterizing the normal operation and the relevant faulty situations. Then, each cluster is surrounded by a spherical envelope to take into account modeling and parameter uncertainties. To perform fault prognosis, the geolocation principal is used to predict the Remaining Useful Lifetime, where the Euclidean distance between normal and faulty clusters, the degradation direction and velocity are calculated. The obtained results, using real wind data, are evaluated using the Prognosis Horizon and the Relative Accuracy metrics.

[1]  Steven X. Ding,et al.  A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches , 2015, IEEE Transactions on Industrial Electronics.

[2]  Paul Fleming,et al.  Use of SCADA Data for Failure Detection in Wind Turbines , 2011 .

[3]  Xiao Yang,et al.  A Particle-Filtering Approach for Remaining Useful Life Estimation of Wind Turbine Gearbox , 2015 .

[4]  M. S. Lebold,et al.  Hybrid reasoning for prognostic learning in CBM systems , 2001, 2001 IEEE Aerospace Conference Proceedings (Cat. No.01TH8542).

[5]  Xavier Guillaud,et al.  Bond graph model of wind turbine blade , 2012 .

[6]  Yi Guo,et al.  Dynamic Analysis of Wind Turbine Planetary Gears Using an Extended Harmonic Balance Approach , 2012 .

[7]  Donghua Zhou,et al.  A model for real-time failure prognosis based on hidden Markov model and belief rule base , 2010, Eur. J. Oper. Res..

[8]  Mark Schwabacher,et al.  A Survey of Data -Driven Prognostics , 2005 .

[9]  Maryam Soleimanzadeh,et al.  State‐space representation of the wind flow model in wind farms , 2014 .

[10]  Ying Peng,et al.  A prognosis method using age-dependent hidden semi-Markov model for equipment health prediction , 2011 .

[11]  Joseph Mathew,et al.  Rotating machinery prognostics. State of the art, challenges and opportunities , 2009 .

[12]  Peter Tavner,et al.  Wind turbine pitch faults prognosis using a-priori knowledge-based ANFIS , 2013, Expert Syst. Appl..

[13]  Douglas E. Adams,et al.  A nonlinear dynamical systems framework for structural diagnosis and prognosis , 2002 .

[14]  Sankalita Saha,et al.  On Applying the Prognostic Performance Metrics , 2009 .

[15]  Zhiwei Gao,et al.  Takagi–Sugeno Fuzzy Model Based Fault Estimation and Signal Compensation With Application to Wind Turbines , 2017, IEEE Transactions on Industrial Electronics.

[16]  C. James Li,et al.  Gear fatigue crack prognosis using embedded model, gear dynamic model and fracture mechanics , 2005 .

[17]  Ying Peng,et al.  Current status of machine prognostics in condition-based maintenance: a review , 2010 .

[18]  Jay Lee,et al.  Wind turbine performance degradation assessment based on a novel similarity metric for machine performance curves , 2016 .

[19]  A. Kusiak,et al.  Modeling wind-turbine power curve: A data partitioning and mining approach , 2017 .

[20]  Peter J Seiler,et al.  Wind Turbine Fault Detection Using Counter-Based Residual Thresholding , 2011 .

[21]  K. Goebel,et al.  Metrics for evaluating performance of prognostic techniques , 2008, 2008 International Conference on Prognostics and Health Management.

[22]  Sirish L. Shah,et al.  Fault Detection and Isolation of a Benchmark Wind Turbine using the Likelihood Ratio Test , 2011 .

[23]  Pierluigi Pisu,et al.  A Comparative Study of Three Fault Diagnosis Schemes for Wind Turbines , 2015, IEEE Transactions on Control Systems Technology.

[24]  Mustapha Ouladsine,et al.  Health Index Extraction Methods for Batch Processes in Semiconductor Manufacturing , 2015, IEEE Transactions on Semiconductor Manufacturing.

[25]  Kathryn E. Johnson,et al.  A tutorial of wind turbine control for supporting grid frequency through active power control , 2012, 2012 American Control Conference (ACC).

[26]  Vicenç Puig,et al.  Fault Diagnosis of an Advanced Wind Turbine Benchmark Using Interval-Based ARRs and Observers , 2015, IEEE Transactions on Industrial Electronics.

[27]  Marcel Staroswiecki,et al.  Model builder using functional and bond graph tools for FDI design , 2005 .

[28]  Ahmed Abu-Siada,et al.  A review of condition monitoring techniques of the wind turbines gearbox and rotor , 2014 .

[29]  Yibing Liu,et al.  Multi-fault detection and failure analysis of wind turbine gearbox using complex wavelet transform , 2016 .

[30]  M. Lydia,et al.  Advanced Algorithms for Wind Turbine Power Curve Modeling , 2013, IEEE Transactions on Sustainable Energy.

[31]  Asok Ray,et al.  Stochastic modeling of fatigue crack dynamics for on-line failure prognostics , 1996, IEEE Trans. Control. Syst. Technol..

[32]  Roberto Tapia-Sánchez,et al.  Wind turbine model simulation: A bond graph approach , 2014, Simul. Model. Pract. Theory.

[33]  Mustapha Ouladsine,et al.  Fault prognosis for batch production based on percentile measure and gamma process: Application to semiconductor manufacturing , 2016 .

[34]  Michel Verhaegen,et al.  Sensor and actuator fault diagnosis for wind turbine systems by using robust observer and filter , 2011 .

[35]  Jiong Tang,et al.  Wind Turbine Gearbox Fault Detection Using Multiple Sensors With Features Level Data Fusion , 2012 .

[36]  T. Yoneyama,et al.  Prognostics performance metrics and their relation to requirements, design, verification and cost-benefit , 2008, 2008 International Conference on Prognostics and Health Management.

[37]  Steven X. Ding,et al.  A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part II: Fault Diagnosis With Knowledge-Based and Hybrid/Active Approaches , 2015, IEEE Transactions on Industrial Electronics.

[38]  Kai Goebel,et al.  A Survey of Artificial Intelligence for Prognostics , 2007, AAAI Fall Symposium: Artificial Intelligence for Prognostics.

[39]  François Monchy,et al.  Maintenance : Méthodes et organisations , 2010 .

[40]  Pierluigi Pisu,et al.  Robust Fault Diagnosis for a Horizontal Axis Wind Turbine , 2011 .

[41]  Zhiwei Gao,et al.  Robust observer-based fault detection via evolutionary optimization with applications to wind turbine systems , 2014, 2014 9th IEEE Conference on Industrial Electronics and Applications.

[42]  Xiaohong Yuan,et al.  Variable amplitude Fourier series with its application in gearbox diagnosis—Part II: Experiment and application , 2005 .

[43]  Ming Yang,et al.  A wavelet approach to fault diagnosis of a gearbox under varying load conditions , 2010 .

[44]  Hamid Reza Karimi,et al.  Wind turbine modeling using the bond graph , 2011, 2011 IEEE International Symposium on Computer-Aided Control System Design (CACSD).

[45]  P. C. Paris,et al.  A Critical Analysis of Crack Propagation Laws , 1963 .

[46]  Belkacem Ould Bouamama,et al.  Bond Graph Approach for Plant Fault Detection and Isolation: Application to Intelligent Autonomous Vehicle , 2014, IEEE Transactions on Automation Science and Engineering.

[47]  M.J. Roemer,et al.  Prognostic enhancements to diagnostic systems for improved condition-based maintenance [military aircraft] , 2002, Proceedings, IEEE Aerospace Conference.