A prediction method for the real-time remaining useful life of wind turbine bearings based on the Wiener process. (November 2018)

• Record Type: Journal Article
• Authors: Hu, Yaogang; Li, Hui; Shi, Pingping; Chai, Zhaosen; Wang, Kun; Xie, Xiangjie; Chen, Zhe
• Is Part Of: Renewable energy. Volume 127(2018)
• Page Start: 452
• Page End: 460
• Publication Date: 2018-11
• Subjects: Wind turbine bearings -- Performance degradation -- Wiener process -- RUL prediction; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2018.04.033
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: A performance degradation model and a real-time remaining useful life (RUL) prediction method are proposed on the basis of temperature characteristic parameters to determine the RUL of wind turbine bearings. First, using the moving average method, the relative temperature data of wind turbine bearings are smoothed, and the temperature trend data are obtained on the basis of the uncertainty of wind speed and wind direction that causes the temperature of wind turbine bearings to vary widely. Second, given that the degradation speed of bearings changes with operational time and uncertain external factors, the performance degradation model is established with the Wiener process. The parameters of this model are obtained through the maximum likelihood estimation method. Third, according to the failure principle of the first temperature monitoring value beyond the first warning threshold, the RUL prediction model for wind turbine bearings is established on the basis of an inverse Gaussian distribution. Finally, the performance degradation process and real-time RUL prediction are demonstrated by predicting the RUL of a practical rear bearing of a wind turbine generator. The comparison of the predicted RUL and actual RUL shows that the proposed model and prediction method are correct and effective. Highlights: Degradation speed of bearings changes with time and uncertain external factors. Physics-based models of RUL prediction for bearings are built difficultly. Degradation model of bearings is built with Wiener process using data-driven method. Proposed method acquires effective predicted RUL only based on temperature data.