상태지수의 경향성 분류에 기반한 풍력발전기 베어링 잔여수명 추정
Estimation of Remaining Useful Life for Bearing of Wind Turbine based on Classification of Trend
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초록
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.
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.
참고문헌 (21)
- Y. Ping, "A prognosis method using age-dependent hidden semi-Markov model for equipment health prediction", Mechanical Systems and Signal Processing, 25, pp.237~252 (2011)
- J. B. Ali, "Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network", Mechanical Systems and Signal Processing, 56-57, pp.150~172 (2015)
- Zhou, J., "Remaining useful life prediction of bearings by a new reinforced memory GRU network", Advanced Engineering Informatics, 53, pp.101682~ (2022)
- 서윤호, "상태지수 통계적 추정을 이용한 베어링 수명 예측", 한국소음진동공학회논문집, 28(3), pp.339~347 (2018), 한국소음진동공학회[DOI]
- 서윤호, "변화 하중 조건에서의 수명 예측을 위한 풍력발전기 베어링 상태지수", 풍력에너지저널, 10(3), pp.22~30 (2019), 한국풍력에너지학회[DOI]
- Seo, Y., "Prediction of bearing life by exponential curve fitting", Proceedings of Asia Pacific Conference of the Prognostics and Health Management Society, pp.193~195 (2017)
- Boskoski P., "Bearing fault prognostics using Renyi entropy based features and Gaussian process models", Mechanical Systems and Signal Processing, 52-53, pp.327~337 (2015)
- Liu, S., "An adaptive prediction approach for rolling bearing remaining useful life based on multistage model with three-source variability", Reliability Engineering & System Safety, 218(Part B), pp.108182~ (2022)
- Rund R., "Detection of Undocumented Changepoints: A Revision of the Two-Phase Regression Model", Journal of Climate, 15, pp.2547~2554 (2002)
- "2013", ~
- Frearnhead, P., "Exact Bayesian Curve Fitting and Signal Segmentation", IEEE Transactions on signal processing, 53(6), pp.2160~2166 (2005)
- "2007", ~
- Alvarez, E., "Bayesian isotonic changepoint analysis", Annals of the Institute of Statistical Mathematics, 61, pp.355~370 (2009)
- Mudelsee M., "Trend analysis of climate time series: A review of methods", Earth-Science Reviews, 190, pp.310~322 (2019)
- Barlow R. E., "Statistical Inference Under Order Restrictions" (1972), John Wiley & Sons
- Pedregosa Fabian, "Scikit-learn:Machine learning in Python", Journal of Machine Learning Research, 12, pp.2825~2830 (2011)
- "ISO 281, Rolling bearings - Dynamic load ratings and rating life", ~ (2007)
- "Bearing Catalog"[URL]
- "KS B 2019, 2021, Rolling bearings - Dynamic load ratings and rating life", ~ (2021)
- Stephens R., "Metal fatigue in engineering" (2001), John Wiley & Sons
- "ISO 20816-21, Mechanical vibration —Measurement and evaluation of machine vibration — Part 21: Horizontal axis wind turbines" (2015)