Prediction of Remaining Useful Life of Wind Turbine Bearings under Non-Stationary Operating Conditions

Lixiao Cao, Zheng Qian, Hamid Zareipour, David Wood, Ehsan Mollasalehi, Shuangshu Tian and Yan Pei
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Lixiao Cao: School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100083, China
Zheng Qian: School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100083, China
Hamid Zareipour: Department of Electrical and Computer Engineering, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada
David Wood: Department of Mechanical and Manufacturing Engineering, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada
Ehsan Mollasalehi: Department of Mechanical and Manufacturing Engineering, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada
Shuangshu Tian: School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100083, China
Yan Pei: School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100083, China

Energies, 2018, vol. 11, issue 12, 1-20

Abstract: Wind-powered electricity generation has grown significantly over the past decade. While there are many components that might impact their useful life, the gearbox and generator bearings are among the most fragile components in wind turbines. Therefore, the prediction of remaining useful life (RUL) of faulty or damaged wind turbine bearings will provide useful support for reliability evaluation and advanced maintenance of wind turbines. This paper proposes a data-driven method combining the interval whitenization method with a Gaussian process (GP) algorithm in order to predict the RUL of wind turbine generator bearings. Firstly, a wavelet packet transform is used to eliminate noise in the vibration signals and extract the characteristic fault signals. A comprehensive analysis of the real degradation process is used to determine the indicators of degradation. The interval whitenization method is proposed to reduce the interference of non-stationary operating conditions to improve the quality of health indicators. Finally, the GP method is utilized to construct the model which reflects the relationship between the RUL and health indicators. The method is assessed using actual vibration datasets from two wind turbines. The prediction results demonstrate that the proposed method can reduce the effect of non-stationary operating conditions. In addition, compared with the support vector regression (SVR) method and artificial neural network (ANN), the prediction accuracy of the proposed method has an improvement of more than 65.8%. The prediction results verify the effectiveness and superiority of the proposed method.

Keywords: remaining useful life (RUL) prediction; wind turbine generator bearing; interval whitenization; Gaussian process; wavelet packet transform (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: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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