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.
Highlights
Wind energy as a renewable energy is growing rapidly around the world
The operation and maintenance (O&M) costs caused by the premature failure of the main components of wind turbine generator can account for 10–20% of the total energy cost for a wind turbine project [1,2]
The wavelet packet transform (WPT) is used to pre-process the raw vibration signals to reduce noise and the raw health indicators are extracted by considering the real degradation process; this is done by analysing the amplitude spectrums in different fault levels and summarizing the variation characteristic of amplitude with the degradation process
Summary
Wind energy as a renewable energy is growing rapidly around the world. the operation and maintenance (O&M) costs caused by the premature failure of the main components of wind turbine generator can account for 10–20% of the total energy cost for a wind turbine project [1,2]. Saidi et al [19] proposed an integrated prognostics method to predict the RUL of wind turbine high-speed shaft bearings, which combined physical degradation models and data-driven approaches. Saidi et al [13] proposed a spectral kurtosis data-driven approach to predict the RUL of wind turbine high-speed shaft bearing, which combined a new condition indicator and SVR method. The present paper selects characteristic features as the health indicators by analysing the degradation trend to improve the prediction effect, instead of only considering the monotonicity and tendency of characteristic features Another challenge of the data-driven methods is on how to deal with non-stationary operating conditions, which are typical of modern wind turbines.
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