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- Title
Data‐driven models for predicting remaining useful life of high‐speed shaft bearings in wind turbines using vibration signal analysis and sparrow search algorithm.
- Authors
Pandit, Ravi; Xie, Weixun
- Abstract
Wind turbine bearings play a crucial role in ensuring the safe and efficient operation of wind turbines. Accurate estimation of the remaining useful life (RUL) of bearings can significantly reduce operating and maintenance costs. In this paper, we propose three advanced data‐driven models to predict the RUL of high‐speed shaft bearings in wind turbines. These models combine the sparrow search algorithm (SSA) with three different regression models, namely support vector machine, random forest (RF) regression and Gaussian process regression. The models are based on features extracted from the vibration signal analysis, and the features are selected based on their monotonicity to evaluate bearing degradation. To optimize the performance of the regression models, all model parameters are tuned using the SSA algorithm. The proposed models are validated using vibration data collected from a real 2 MW commercial wind turbine. Our results demonstrate that the proposed models are effective in predicting the RUL of wind turbine bearings, and the SSA algorithm improves the accuracy of the predictions.
- Subjects
REMAINING useful life; BEARINGS (Machinery); WIND turbines; SEARCH algorithms; KRIGING; FEATURE extraction; COST estimates
- Publication
Energy Science & Engineering, 2023, Vol 11, Issue 12, p4557
- ISSN
2050-0505
- Publication type
Article
- DOI
10.1002/ese3.1597