Abstract
For wind turbine gearbox fault diagnosis problem, we propose a multi-classification least squares support vector machines (MCLSSVM) model. According to failure mechanism and vibration characteristics of gearbox, it investigates some formulas of fault diagnosis. Through the combination of voting method and decision tree, it constructs the MCLSSVM decision-making structure, and then it is applied on the fault diagnosis of wind turbine gearbox. Tests show that MCLSSVM can be effectively used in the fault diagnosis of wind turbine gearbox. It solves the studying problem of small sample, and overcomes the shortcoming of artificial neural network (ANN) when it is used in fault diagnosis.
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© 2012 Springer-Verlag GmbH Berlin Heidelberg
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Jiao, B., Xu, Z. (2012). Multi-Classification LSSVM Application in Fault Diagnosis of Wind Power Gearbox. In: Zhang, T. (eds) Mechanical Engineering and Technology. Advances in Intelligent and Soft Computing, vol 125. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27329-2_38
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DOI: https://doi.org/10.1007/978-3-642-27329-2_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27328-5
Online ISBN: 978-3-642-27329-2
eBook Packages: EngineeringEngineering (R0)