지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
오류제보하기
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.
목차
등록된 정보가 없습니다.
참고문헌 (17)
참고문헌 신청
Gebraeel. N. / 2005 / Residual-life distributions from component degradation signals: A Bayesian approach / IIE Transactions 37:543~557
Boskoski, P. / 2015 / Bearing fault prognostics using Renyi entropy based features and Gaussian process models / Mechanical Systems and Signal Processing 52-53:327~337
Ali, J. B. / 2015 / Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network / Mechanical Systems and Signal Processing 56-57:150~172
Peng, Y. / 2011 / A prognosis method using age-dependent hidden semi-Markov model for equipment health prediction / Mechanical Systems and Signal Processing 25:237~252
Department of Energy / 2015 / Statistics Show Bearing Problems Cause the Majority of Wind Turbine Gearbox Failures
Boskoski, P. / 2015 / Bearing fault prognostics using Renyi entropy based features and Gaussian process models / Mechanical Systems and Signal Processing 52-53:327~337
Ali, J. B. / 2015 / Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network / Mechanical Systems and Signal Processing 56-57:150~172
Peng, Y. / 2011 / A prognosis method using age-dependent hidden semi-Markov model for equipment health prediction / Mechanical Systems and Signal Processing 25:237~252
Department of Energy / 2015 / Statistics Show Bearing Problems Cause the Majority of Wind Turbine Gearbox Failures
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