Fault Diagnosis of Wind Turbine Gearbox Based on Least Square Support Vector Machine with Genetic Algorithm

Wen Qing Zhao; Rui Cai; Li Wei Wang; De Wen Wang

doi:10.4028/www.scientific.net/AMR.846-847.620

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 846-847Fault Diagnosis of Wind Turbine Gearbox Based on...

Fault Diagnosis of Wind Turbine Gearbox Based on Least Square Support Vector Machine with Genetic Algorithm

Abstract:

Gearbox affect the normal operation of the wind turbines, to study the fault diagnosis, support vector method was used. Parameters selection is very important and decides the fault diagnosis precision. In order to overcome the blindness of man-made choice of the parameters in least squares support vector machine (LSSVM) and improve the accuracy and efficiency of fault diagnosis, a method based on LSSVM trained by genetic algorithm was proposed. This method searches the optimized parameters in LSSVM by taking advantage of the genetic algorithms powerful global searching ability. The research is provided using this method on the fault diagnosis of wind turbine gearbox and compared with the diagnostic method of LSSVM. The experimental results show that the method achieves a higher diagnostic accuracy.

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Periodical:

Advanced Materials Research (Volumes 846-847)

Pages:

620-623

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.846-847.620

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Online since:

November 2013

Authors:

Wen Qing Zhao*, Rui Cai, Li Wei Wang, De Wen Wang

Keywords:

Fault Diagnosis, Gearbox, Genetic Algorithm (GA), Least Square Support Vector Machine, Wind Turbine (WT)

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* - Corresponding Author

References

[1] FENG Zhipeng, ZHAO Leilei, CHU Fulei. Proceedings of the CSEE, 2013, 33(5): 119-127(In Chinese).

Google Scholar

[2] ZHANG Qing: East China University of Science and Technology, 2013 (In Chinese).

Google Scholar

[3] GUO Peng, Infield D, YANG Xiyun: Proceedings of the CSEE, 2011, 31(32): 129-136 (In Chinese).

Google Scholar

[4] LONG Quan, Liu Yongqian, Yang Yongping: Acta Energiae Solaris Sinica, 2012, 33(1): 120-125(In Chinese).

Google Scholar

[5] CORTES C, VAPNIK V. Support-vector network[J]. Machine Learning, 1995, 20(3): 273-297.

Google Scholar

[6] ZHANG Zhe, ZHAO Wenqing, ZHU Yongli: Electric Power Automation Equipment. 2010, 30(4): 81-84(In Chinese).

Google Scholar

[7] Suykens JAK, Vandewalle J: Least square support vector machines classifiers[J]. Neural Processing Letters(S1370-4621), 1999, 9(3): 293-300.

DOI: 10.1023/a:1018628609742

Google Scholar

[8] YAN Weiwu, SHAO Huihe: Control and Decision, 2003, 18(3): 358-360. (In Chinese).

Google Scholar