Fault Diagnosis Method for Wind Turbine Gearboxes Based on IWOA-RF

Mingzhu Tang, Zixin Liang, Huawei Wu and Zimin Wang
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Mingzhu Tang: School of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, China
Zixin Liang: School of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, China
Huawei Wu: Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei University of Arts and Science, Xiangyang 441053, China
Zimin Wang: School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China

Energies, 2021, vol. 14, issue 19, 1-13

Abstract: A fault diagnosis method for wind turbine gearboxes based on undersampling, XGBoost feature selection, and improved whale optimization-random forest (IWOA-RF) was proposed for the problem of high false negative and false positive rates in wind turbine gearboxes. Normal samples of raw data were subjected to undersampling first, and various features and data labels in the raw data were provided with importance analysis by XGBoost feature selection to select features with higher label correlation. Two parameters of random forest algorithm were optimized via the whale optimization algorithm to create a fitness function with the false negative rate ( FNR ) and false positive rate ( FPR ) as evaluation indexes. Then, the minimum fitness function value within the given scope of parameters was found. The WOA was controlled by the hyper-parameter ? to optimize the step size. This article uses the variant form of the sigmoid function to alter the change trend of the WOA hyper-parameter ? from a linear decline to a rapid decline first and then a slow decline to allow the WOA to be optimized. In the initial stage, a larger step size and step size change rate can make the model progress to the optimization target faster, while in the later stage of optimization, a smaller step size and step size change rate allows the model to more accurately find the minimum value of the fitness function. Finally, two hyper-parameters, corresponding to the minimum fitness function value, were substituted into a random forest algorithm for model training. The results showed that the method proposed in this paper can significantly reduce the false negative and false positive rates compared with other optimization classification methods.

Keywords: wind turbine; gearbox fault; XGBoost feature selection; whale optimization; random forest (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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