Journal of Wind Energy (풍력에너지저널)

Korea Wind Energy Association (한국풍력에너지학회)
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Estimation of Remaining Useful Life for Bearing of Wind Turbine based on Classification of Trend

상태지수의 경향성 분류에 기반한 풍력발전기 베어링 잔여수명 추정

  • Yun-Ho Seo ;
  • SangRyul Kim ;
  • Pyung-Sik Ma ;
  • Jung-Han Woo ;
  • Dong-Joon Kim
  • 서윤호 (한국기계연구원, 기계시스템안전연구본부) ;
  • 김상렬 (한국기계연구원, 기계시스템안전연구본부) ;
  • 마평식 (한국기계연구원, 기계시스템안전연구본부) ;
  • 우정한 (한국기계연구원, 기계시스템안전연구본부) ;
  • 김동준 (한국기계연구원, 기계시스템안전연구본부)
  • Received : 2023.05.03
  • Accepted : 2023.08.05
  • Published : 2023.09.30
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Abstract

The reduction of operation and maintenance (O&M) costs is a critical factor in determining the competitiveness of wind energy. Predictive maintenance based on the estimation of remaining useful life (RUL) is a key technology to reduce logistic costs and increase the availability of wind turbines. Although a mechanical component usually has sudden changes during operation, most RUL estimation methods use the trend of a state index over the whole operation period. Therefore, overestimation of RUL causes confusion in O&M plans and reduces the effect of predictive maintenance. In this paper, two RUL estimation methods (load based and data driven) are proposed for the bearings of a wind turbine with the results of trend classification, which differentiates constant and increasing states of the state index. The proposed estimation method is applied to a bearing degradation test, which shows a conservative estimation of RUL.

Keywords

Acknowledgement

본 연구는 2022년도 산업통상자원부의 재원으로 한국에너지기술평가원(KETEP)의 지원을 받아 수행한 연구 과제(No. 20220710100020 공존 적합 해상풍력 단지설계 및 수중소음 관리 기술 개발) 및 2023년도 한국기계연구원 기본사업(NK244B)의 지원으로 수행되었습니다.

References

  1. Y. Ping et. al., 2011, A prognosis method using age-dependent hidden semi-Markov model for equipment health prediction, Mechanical Systems and Signal Processing, Vol. 25, pp. 237~252.
  2. J. B. Ali et. al., 2015, Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network, Mechanical Systems and Signal Processing, Vol. 56-57, pp. 150~172.
  3. Zhou, J. et. al., 2022, Remaining useful life prediction of bearings by a new reinforced memory GRU network, Advanced Engineering Informatics, Vol. 53, 101682.
  4. Seo, Y., Kim, S., Kim, B. and Ma, P., 2018, "Life Prediction of Bearing by Statistical Estimation of State Index," Transaction of Korean Society of Noise and Vibration Engineering, Vol. 28, No. 3, pp. 339-347.
  5. Seo, Y., Kim, S., Kim, B. and Ma, P., 2019, "State Index of Bearing of Wind Turbine under Variable Loading Condition to Predict Remaining Useful Life," Journal of Wind Energy, Vol. 10, No. 3, pp. 22-30(in Korean).
  6. Seo, Y., Kim, S., Kim, B. and Ma, P., 2017, "Prediction of bearing life by exponential curve fitting," Proceedings of Asia Pacific Conference of the Prognostics and Health Management Society, Jeju, Korea, pp. 193~195.
  7. Boskoski P., 2015, Bearing fault prognostics using Renyi entropy based features and Gaussian process models, Mechanical Systems and Signal Processing, Vol. 52-53, pp. 327-337.
  8. Liu, S., Fan, L. 2022, An adaptive prediction approach for rolling bearing remaining useful life based on multistage model with three-source variability, Reliability Engineering & System Safety, Vol. 218, Part B, 108182.
  9. Rund R., Reeves J., 2002, Detection of Undocumented Changepoints: A Revision of the Two-Phase Regression Model, Journal of Climate, Vol. 15, pp. 2547-2554.
  10. Matterson, D., James, N., 2013, A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data, arXiv:1306.4933v2 [stat.ME].
  11. Frearnhead, P., 2005, Exact Bayesian Curve Fitting and Signal Segmentation, IEEE Transactions on signal processing, Vol. 53, No. 6, pp. 2160-2166.
  12. Adam, R., Mackay, D., 2007, Bayesian Online Changepoint Detection, arXiv:0710.3742v1 [stat.ML].
  13. Alvarez, E., Dey, D., 2009, Bayesian isotonic changepoint analysis, Annals of the Institute of Statistical Mathematics, Vol. 61, pp. 355-370/
  14. Mudelsee M., 2019, Trend analysis of climate time series: A review of methods, Earth-Science Reviews, Vol. 190, pp. 310-322.
  15. Barlow R. E., Bartholomew D. J., Bremner J. M. and Brunk H. D., 1972, Statistical Inference Under Order Restrictions. John Wiley & Sons, New York.
  16. Pedregosa Fabian et al., 2011, "Scikit-learn: Machine learning in Python," Journal of Machine Learning Research. Vol. 12, pp. 2825~2830
  17. ISO 281, 2007, Rolling bearings - Dynamic load ratings and rating life
  18. Bearing Catalog, http://www.jp.nsk.com/app02/NSKOnlineCatalog/en/index.jsp#
  19. KS B 2019, 2021, Rolling bearings - Dynamic load ratings and rating life
  20. Stephens R., 2001, Metal fatigue in engineering, John Wiley & Sons.
  21. ISO 20816-21, 2015, Mechanical vibration -Measurement and evaluation of machine vibration - Part 21: Horizontal axis wind turbines.