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변화 하중 조건에서의 수명 예측을 위한 풍력발전기 베어링 상태지수

State Index of Bearing of Wind Turbine under Variable Loading Conditions to Predict Remaining Useful Life

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주제분류기타공학

초록

This paper suggests an effective state index that represents a quantitative value for damage in order to predict the life of a bearing for a wind turbine. Two fault modes, lubrication failure and rolling element failure, are artificially made in the laboratory. Then, several degradation tests including the measurement of vibration, temperature and torque are performed under both a constant loading condition and stepwise variable loading condition. With the analysis of the measured vibration under the constant loading condition, the root mean square (RMS) value and a vibration component of bearing fault frequency are chosen as good indicators for the prediction of life. Finally, a state index is proposed by the weighted combination of vibration, temperature and torque. The proposed state index is monotonic, increasing as time goes by under the stepwise variable loading condition as well as constant loading condition. Because the monotonicity is a good characteristic to predict the life of bearings in the machine parts of a wind turbine, it is expected that the proposed state index will be helpful for the prediction of the life of bearings.

Abstract

This paper suggests an effective state index that represents a quantitative value for damage in order to predict the life of a bearing for a wind turbine. Two fault modes, lubrication failure and rolling element failure, are artificially made in the laboratory. Then, several degradation tests including the measurement of vibration, temperature and torque are performed under both a constant loading condition and stepwise variable loading condition. With the analysis of the measured vibration under the constant loading condition, the root mean square (RMS) value and a vibration component of bearing fault frequency are chosen as good indicators for the prediction of life. Finally, a state index is proposed by the weighted combination of vibration, temperature and torque. The proposed state index is monotonic, increasing as time goes by under the stepwise variable loading condition as well as constant loading condition. Because the monotonicity is a good characteristic to predict the life of bearings in the machine parts of a wind turbine, it is expected that the proposed state index will be helpful for the prediction of the life of bearings.

참고문헌 (17)

  1. Gebraeel. N., "Residual-life distributions from component degradation signals: A Bayesian approach", IIE Transactions, 37, pp.543~557 (2005)
  2. 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)
  3. Ali, J. B., "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)
  4. Peng, Y., "A prognosis method using age-dependent hidden semi-Markov model for equipment health prediction", Mechanical Systems and Signal Processing, 25, pp.237~252 (2011)
  5. Statistics Show Bearing Problems Cause the Majority of Wind Turbine Gearbox Failures, "Department of Energy", ~ (2015)
  6. Li, C., "Coupled lateral-torsional-axial vibrations of a helicalgear-rotor-bearing system", Acta Mechanica Sinica, 30(5), pp.746~761 (2014)
  7. Gearbox Reliability Collaborative Gearbox 1 Failure Analysis Report, "Errichello, R.", National Renewable Energy Laboratory, ~, pp.NREL (2012), SR-5000-53062
  8. Randall, R., "Vibration-based Condition Monitoring: Industrial, Aerospace and Automotive Applications" (2010), John Wiley & Sons
  9. "Prognostics Data Repository" (2017)[URL]
  10. "Bearing Data Center" (2017)[URL]
  11. "Data Repository for PHMAP 2017 Data Challenge" (2017)[URL]
  12. Seo, Y., "Life Prediction of Bearing for the Drive Train of a Wind Turbine", Proceedings of European Conference of the Prognostics and Health Management Society (2018)
  13. 서윤호, "상태지수 통계적 추정을 이용한 베어링 수명 예측", 한국소음진동공학회논문집, 28(3), pp.339~347 (2018), 한국소음진동공학회[DOI]
  14. ISO, "Condition monitoring and diagnostics of machines - Prognostics - Part 1:General guidelines", ISO 13381-1, ~ (2004)
  15. Zhou, H., "Detection and diagnosis of bearing faults using shift-invariant dictionary learning and hidden Markov model", Mechanical Systems and Signal Processing, 72-73, pp.65~79 (2016)
  16. 박성택, "위너 필터와 충격 펄스 카운팅을 이용한 저속 기계용 구름 베어링의 결함 검출", 한국소음진동공학회논문집, 22(12), pp.1227~1236 (2012), 한국소음진동공학회[DOI]
  17. IEC, "Wind energy generation systems -Part 25-6: Communications for monitoring and control of wind power plants - Logical node classes and data classes for condition monitoring, IEC 61400-25-6", International Electrotechnical Commission, ~ (2016)

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