计量学报2018，Vol.39Issue(1)：89-93,5.DOI:10.3969/j.issn.1000-1158.2018.01.19
基于相关主成分分析和极限学习机的风电机组主轴承状态监测研究
Research of Wind Turbine Main Bearing Condition Monitoring Based on Correlation PCA and ELM
摘要
Abstract
A condition monitoring method of main bearing based on correlation coefficient method,principal component analysis and extreme learning machine are proposed.First,correlation coefficient method is used to select the initial input variables which is related to the main bearing temperature in the supervisory control and data acquistion system.Further,to eliminate the correlation and the redundancy between the selected variables,principal component analysis is applied to reduce the dimension.Again,the extreme learning machine is used to construct the normal behavior model of the main bearing temperature and predict the temperature.Last,a moving window and kernel density estimation method is used to analyze the residual,and based on the measured data to simulate main bearing fault conditions.The experimental results demonstrate that the proposed method can effectively achieve the potential failure prediction and avoid serious fault of the main bearing.关键词
计量学/风电机组/主轴承/状态监测/极限学习机/温度预测/残差分析Key words
metrology/wind turbines/main bearing/condition monitoring/extreme learning machine/temperature prediction/residual analysis分类
通用工业技术引用本文复制引用
何群,王红,江国乾,谢平,李继猛,王腾超..基于相关主成分分析和极限学习机的风电机组主轴承状态监测研究[J].计量学报,2018,39(1):89-93,5.基金项目
国家自然科学基金(51505415) （51505415）
河北省自然科学基金(F2016203421) （F2016203421）
河北省高等学校科学技术研究重点项目(ZD20131080) （ZD20131080）
