Fault detection in wind turbine system using wavelet transform: Multi-resolution analysis

In this paper, the wavelet transform theory is used to fault diagnosis for wind turbine benchmark model, considering its characteristics of multi-resolution and thresholds. The paper emphasizes de-noising based on wavelet transform in signal of generated residue in order to detect faults. Executing wavelet transformation, faults in the wind turbine can be detected. We have attempted to show how wavelet transform can be used to detect faults.

[1]  Silvio Simani,et al.  Noise rejection in parameters identification for piecewise linear fuzzy models , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[2]  Peter Fogh Odgaard,et al.  Observer Based Detection of Sensor Faults in Wind Turbines , 2009 .

[3]  Di Chen,et al.  Wavelet-Based Data Reduction Techniques for Process Fault Detection , 2006, Technometrics.

[4]  Silvio Simani,et al.  Data—Driven Approach for Wind Turbine Actuator and Sensor Fault Detection and Isolation , 2011 .

[5]  Wenyi Liu,et al.  Wind turbine fault diagnosis based on Morlet wavelet transformation and Wigner-Ville distribution , 2010 .

[6]  C. Fantuzzi,et al.  Identification of piecewise affine models in noisy environment , 2002 .

[7]  Peter Tavner,et al.  Condition monitoring and fault diagnosis of a wind turbine synchronous generator drive train , 2009 .

[8]  Silvio Simani,et al.  Parameter identification for piecewise-affine fuzzy models in noisy environment , 1999, Int. J. Approx. Reason..

[9]  Suzanne Lesecq,et al.  Fault Isolation Based on Wavelets Transform , 2007 .

[10]  Christophe Aubrun,et al.  Set-point reconfiguration approach for the FTC of wind turbines , 2011 .

[11]  Sung-Hoon Ahn,et al.  Condition monitoring and fault detection of wind turbines and related algorithms: A review , 2009 .

[12]  Carl Taswell,et al.  The what, how, and why of wavelet shrinkage denoising , 2000, Comput. Sci. Eng..

[13]  J. Spencer.  Ten lectures on the probabilistic method , 1987 .

[14]  Robert Babuska,et al.  Fuzzy Modeling for Control , 1998 .

[15]  Ioana Fagarasan,et al.  Applications of fault detection methods to industrial processes , 2008 .

[16]  Mostefa Mesbah,et al.  Wavelet Decomposition for the Detection and Diagnosis of Faults in Rolling Element Bearings , 2009 .

[17]  Silvio Simani.  Application of a Data-Driven Fuzzy Control Design to a Wind Turbine Benchmark Model , 2012, Adv. Fuzzy Syst..

[18]  Vicente Alarcón Aquino,et al.  A comparative simulation study of wavelet based denoising algorithms , 2005, 15th International Conference on Electronics, Communications and Computers (CONIELECOMP'05).

[19]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[20]  Tommy W. S. Chow,et al.  Induction machine fault diagnostic analysis with wavelet technique , 2004, IEEE Transactions on Industrial Electronics.

[21]  Stéphane Mallat,et al.  Characterization of Signals from Multiscale Edges , 2011, IEEE Trans. Pattern Anal. Mach. Intell..