Abstract
Vibration signals contain abundant information that reflects the health status of wind turbine high-speed shaft bearings ((HSSBs). Accurate health assessment and remaining useful life (RUL) prediction are the keys to the scientific maintenance of wind turbines. In this paper, a method based on the combination of a comprehensive evaluation function and a self-organizing feature map (SOM) network is proposed to construct a health indicator (HI) curve to characterizes the health state of HSSBs. Considering the difficulty in obtaining life cycle data of similar equipment in a short time, the exponential degradation model is selected as the degradation trajectory of HSSBs on the basis of the constructed HI curve, the Bayesian update model, and the expectation–maximization (EM) algorithm are used to predict the RUL of HSSBs. First, the time domain, frequency domain, and time–frequency domain degradation features of HSSBs are extracted. Second, a comprehensive evaluation function is constructed and used to select the degradation features with good performance. Third, the SOM network is used to fuse the selected degradation features to construct a one-dimensional HI curve. Finally, the exponential degradation model is selected as the degradation trajectory of HSSBs, and the Bayesian update and EM algorithm are used to predict the RUL of the HSSB. The monitoring data of a wind turbine HSSB in actual operation is used to validate the model. The HI curve constructed by the method in this paper can better reflect the degradation process of HSSBs. In terms of life prediction, the method in this paper has better prediction accuracy than the SVR model.
Highlights
This work can be summarized into four steps: (1) extract the time domain, frequency domain, and time–frequency domain degradation characteristics of the vibration signal of the wind turbine high-speed shaft bearings (HSSBs); (2) use the comprehensive evaluation function constructed by monotonicity, correlation, and robustness to screen the degradation features; (3) use the self-organizing mapping (SOM) network to fuse the selected degradation features and construct the health indicator (HI) curve; (4) combine the Bayesian update and the expectation–maximization algorithm on the basis of the constructed HI curve to predict the remaining useful life (RUL) of the wind turbine HSSB
To verify the advantages of this method, RMS, principal component analysis (PCA), and the HI curves constructed by SOM network fusion of all degradation features are used for comparative analysis
The HI curve constructed by the method in this paper is an exponential function curve, and the HI curve can be described by the exponential degradation model.shaft bearing HI curve
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Due to the harsh operating environment and the complex transmission system of wind turbines, the vibration signals of each component are coupled and superimposed with each other For these reasons, a comprehensive evaluation function is constructed to select excellent time domain, frequency domain, and time–frequency domain degradation features. It is often difficult to obtain sufficient historical degradation data for similar equipment, especially for newly operating equipment and expensive equipment Considering these problems, this work uses the Bayesian update and expectation–maximization algorithm to predict the RUL of wind turbine HSSBs. This work can be summarized into four steps: (1) extract the time domain, frequency domain, and time–frequency domain degradation characteristics of the vibration signal of the wind turbine HSSB; (2) use the comprehensive evaluation function constructed by monotonicity, correlation, and robustness to screen the degradation features; (3) use the SOM network to fuse the selected degradation features and construct the HI curve;.
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