Username   Password       Forgot your password?  Forgot your username? 


Classification of Various Wind Turbine Blade Faults through Vibration Signals Using Hyperpipes and Voting Feature Intervals Algorithm

Volume 13, Number 3, May 2017 - Paper 1 - pp. 247-258
DOI: 10.23940/ijpe.17.03.p1.247258

Joshuva. A.* and Sugumaran. V.

School of Mechanical and Building Sciences (SMBS), VIT University, Chennai Campus, Vandalur-Kelambakkam Road, Chennai 600127, India.

(Submitted on January 1, 2017; Revised on March 28, 2017; Accepted on April 12, 2017)


Wind energy has turned into a massive contender of traditional fossil fuel energy. Due to environmental conditions and over-time run conditions, wind turbine blades are prompt to different vibrations which cause damage to the blades. This paper presents an algorithmic classification of various blade fault conditions like blade bend, blade cracks, blade erosion, hub-blade loose connection and pitch angle twist using vibration signals. Initially histogram features were extracted from the vibration data and classified using machine learning algorithms like hyperpipes (HP) and voting feature intervals (VFI) algorithm. The performance of these algorithms were compared with respect to classification accuracy and better algorithm was suggested for fault prediction on wind turbine blades.


References: 26

1. Ciang CC, Lee JR, Bang HJ. Structural health monitoring for a wind turbine system: a review of damage detection methods. Measurement Science and Technology. 2008 Oct 13;19(12):122001.
2. Tummala A, Velamati RK, Sinha DK, Indraja V, Krishna VH. A review on small scale wind turbines. Renewable and Sustainable Energy Reviews. 2016 Apr 30;56:1351-71.
3. Joshuva A, Sugumaran V. Fault diagnostic methods for wind turbine: A review. Asian Research Publishing Network (ARPN) Journal of Engineering and Applied Sciences. 2016 Apr;11(7):4654-68.
4. Wang Y, Liang M, Xiang J. Damage detection method for wind turbine blades based on dynamics analysis and mode shape difference curvature information. Mechanical Systems and Signal Processing. 2014 Oct 3;48(1):351-67.
5. Pratumnopharat P, Leung PS, Court RS. Wavelet transform-based stress-time history editing of horizontal axis wind turbine blades. Renewable Energy. 2014 Mar 31;63:558-75.
6. Saravanakumar R, Jena D. Validation of an integral sliding mode control for optimal control of a three blade variable speed variable pitch wind turbine. International Journal of Electrical Power & Energy Systems. 2015 Jul 31;69:421-9.
7. Bitkina O, Kang KW, Lee JH. Experimental and theoretical analysis of the stress–strain state of anisotropic multilayer composite panels for wind turbine blade. Renewable Energy. 2015 Jul 31;79:219-26.
8. Liu W. Design and kinetic analysis of wind turbine blade-hub-tower coupled system. Renewable Energy. 2016 Aug 31;94:547-57.
9. Pourrajabian A, Afshar PA, Ahmadizadeh M, Wood D. Aero-structural design and optimization of a small wind turbine blade. Renewable Energy. 2016 Mar 31;87:837-48.
10. Wang Y, Sun X, Dong X, Zhu B, Huang D, Zheng Z. Numerical investigation on aerodynamic performance of a novel vertical axis wind turbine with adaptive blades. Energy Conversion and Management. 2016 Jan 15;108:275-86.
11. Rezaei MM, Behzad M, Moradi H, Haddadpour H. Modal-based damage identification for the nonlinear model of modern wind turbine blade. Renewable Energy. 2016 Aug 31;94:391-409.
12. Joshuva A, Sugumaran V. A data driven approach for condition monitoring of wind turbine blade using vibration signals through best-first tree algorithm and functional trees algorithm: A comparative study. ISA transactions. 2017 Mar 31;67:160-72.
13. Joshuva A, Sugumaran V, Amarnath M. Selecting kernel function of Support Vector Machine for fault diagnosis of roller bearings using sound signals through histogram features. International Journal of Applied Engineering Research. 2015;10(68):482-7.
14. Mitchell TM. Machine learning. 1997. Burr Ridge, IL: McGraw Hill. 1997;45:37.
15. Witten IH, Frank E. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann; 2005.
16. Villacampa O. Feature Selection and Classification Methods for Decision Making: A Comparative Analysis. 2015.
17. Sakthivel NR, Sugumaran V, Babudevasenapati S. Vibration based fault diagnosis of monoblock centrifugal pump using decision tree. Expert Systems with Applications. 2010 Jun 30;37(6):4040-9.
18. Sugumaran V, Ramachandran KI. Fault diagnosis of roller bearing using fuzzy classifier and histogram features with focus on automatic rule learning. Expert Systems with Applications. 2011 May 31;38(5):4901-7.
19. Güvenir HA, Emeksiz N. An expert system for the differential diagnosis of erythemato-squamous diseases. Expert Systems with Applications. 2000 Jan 31;18(1):43-9.
20. Deeb ZA, Devine T, Geng Z. Randomized Decimation HyperPipes. Citeseer. 2010.
21. Demiröz G, Güvenir HA. Classification by voting feature intervals. In European Conference on Machine Learning 1997 Apr 23 (pp. 85-92). Springer Berlin Heidelberg.
22. Ali F, Hayat M. Classification of membrane protein types using Voting Feature Interval in combination with Chou’s Pseudo Amino Acid Composition. Journal of theoretical biology. 2015 Nov 7;384:78-83.
23. Joshuva A, Sugumaran V. Wind Turbine Blade Fault Diagnosis Using Vibration Signals through Decision Tree Algorithm. Indian Journal of Science and Technology. 2016 Dec 29;9(48).
24. Joshuva A, Sugumaran V, Amarnath M, Lee SK. Remaining Life-Time Assessment of Gear Box Using Regression Model. Indian Journal of Science and Technology. 2016 Dec 28;9(47).
25. Sugumaran V, Jain D, Amarnath M, Kumar H. Fault diagnosis of helical gearbox using Decision Tree through vibration signals. International Journal of Performability Engineering. 2013 Mar 1;9(2):221-34.
26. Elasha F, Teixeira Ja. Condition monitoring philosophy for tidal turbines. International Journal of Performability Engineering. 2014 Jul 1;10(5):521.


Click here to download the paper.

Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

This site uses encryption for transmitting your passwords.