Bearing Fault Diagnosis of a Wind Turbine Using Maximum Likelihood Detection

摘要：

Bearings are the most frequently used components in a wind turbine. As such, bearing Fault Detection is an imperative part of preventive maintenance procedures of a wind turbine. This paper presents a Maximum likelihood method to implement bearing fault diagnosis. This set extracts the amplitude and frequency modulations of the vibration signals measure from a wind turbine system. As the amplitude demodulation is inherent in this set, the fault frequency can be detected from the spectrum of the transformed signal. The effectiveness of this method has been validated by using simulated signal and experimental data.

关键词： bearing fault diagnosis bearing wind turbine maximum likelihood detection

作者: Shenggang Yang Xiaoli Li Ming Liang

作者单位: Key Lab of Industrial Computer Control Engineering of Hebei Province, Institute of Electrical Engine Department of Mechanical Engineering University of Ottawa, 770 King Edward Avenue, Ottawa, Ontario,

会议类型: 国际会议

会议名称: 2011 International Conference on Electronic & Mechanical Engineering and Information Technology(EMEIT 2011)(2011年机电工程与信息技术国际会议)

会议地点: 哈尔滨

会议语种:英文

页码: 3039-3042

在线出版日期: 2011-08-12（万方平台首次上网日期，不代表论文的发表时间）