Health Assessment and Remaining Useful Life Prediction of Wind Turbine High-Speed Shaft Bearings
| Autor: | Tusongjiang Kari, Xinyan Zhang, Wei Hu, Zhenen Li |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Technology
Control and Optimization Computer science 020209 energy assessment Energy Engineering and Power Technology 02 engineering and technology computer.software_genre Turbine wind turbines Expectation–maximization algorithm 0202 electrical engineering electronic engineering information engineering Feature (machine learning) Time domain Electrical and Electronic Engineering Engineering (miscellaneous) Wind power Renewable Energy Sustainability and the Environment business.industry 020208 electrical & electronic engineering self-organizing feature map prediction Evaluation function exponential degradation model comprehensive evaluation function Frequency domain Trajectory Data mining business computer Energy (miscellaneous) |
| Zdroj: | Energies, Vol 14, Iss 4612, p 4612 (2021) Energies Volume 14 Issue 15 |
| ISSN: | 1996-1073 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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