Abstract
Wind turbines are widely installed as the new source of cleaner energy production. Dynamic and random stress imposed on the generator bearing of a wind turbine may lead to overheating and failure. In this paper, a data-driven approach for condition monitoring of generator bearings using temporal temperature data is presented. Four algorithms, the support vector regression machine, neural network, extreme learning machine, and the deep belief network are applied to model the bearing behavior. Comparative analysis of the models has demonstrated that the deep belief network is most accurate. It has been observed that the bearing failure is preceded by a change in the prediction error of bearing temperature. An exponentially-weighted moving average (EWMA) control chart is deployed to trend the error. Then a binary vector containing the abnormal errors and the normal residuals are generated for classifying failures. LS-SVM based classification models are developed to classify the fault bearings and the normal ones. The proposed approach has been validated with the data collected from 11 wind turbines.
Highlights
IntroductionContinuous operations in all environmental conditions contribute to failures of wind turbine components, assemblies, and systems
Wind energy is the fastest growing form of renewable energy
The condition-monitoring framework proposed in this study has provided promising results using field supervisory control and data acquisition (SCADA) data
Summary
Continuous operations in all environmental conditions contribute to failures of wind turbine components, assemblies, and systems. The generator of a wind turbine is one of the most failure-prone assemblies due to the variable loads (Kusiak and Verma, 2012). A solution for effective condition monitoring of generator bearings and early identification of failure symptoms is needed. Vibration analysis and data-driven approaches have been applied for condition monitoring of generator bearings (Yang et al, 2018). The frequently used classical vibration analysis approaches include Fourier transformation (Klein et al, 2001), wavelet transform (Yan et al, 2014), Hilbert-Huang transform (Peng et al, 2005; Huang and Wu 2008), and empirical model decomposition (EMD) (Huang et al, 2008). Other models have been developed. Teng et al (2016) utilized a complex
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