A self-data-driven method for remaining useful life prediction of wind turbines considering continuously varying speeds. (15th February 2022)

• Record Type: Journal Article
• Authors: Li, Naipeng; Xu, Pengcheng; Lei, Yaguo; Cai, Xiao; Kong, Detong
• Is Part Of: Mechanical systems and signal processing. Volume 165(2022)
• Publication Date: 2022-02-15
• Subjects: Wind turbines -- Remaining useful life prediction -- Self-data-driven -- Cumulative degradation model -- Continuously varying speeds; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2021.108315
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: The RUL of a wind turbine is predicted driven by its own condition monitoring data. A generalized degradation model is constructed under continuously varying speeds. The impacts of varying speeds on degradation rates and observations are considered. The first degradation time and first predicting time are detected automatically. The best fitting model is selected adaptively according to the degradation trend. Abstract: Predictive maintenance is one of the most promising ways to reduce the operation and maintenance (O&M) costs of wind turbines (WTs). Remaining useful life (RUL) prediction is the basis for predictive maintenance decision. Self-data-driven methods predict the RUL of a WT driven by its own condition monitoring data without depending on failure event data. Therefore, they are applicable in industrial cases where no sufficient failure event data is available. One challenging issue for RUL prediction of WTs is that they generally suffer from varying rotating speeds. The speed variation has serious impact on the degradation rates as well as the amplitudes of state observations. To deal with this issue, this paper proposes a self-data-driven RUL prediction method for WTs considering continuously varying speeds. In the method, a generalized cumulative degradation model is constructed to describe the degradation process of WTs under continuously varying speeds. A baseline transformation algorithm is developed to transform health state observations under varying speeds into a baseline speed. A continuous trigging algorithm is employed to determine the first degradation time (FDT) for degradation modeling and the first predicting time (FPT) for RUL prediction. The best fitting model is selected adaptively to keep in line with the degradation trend of interest. The effectiveness of the method is demonstrated using a simulation case study and an industrial case study.