Fault Diagnosis of Wind Turbine Rolling Bearing Based on Wavelet and Hilbert transforms

摘要：

Rolling bearing is not only one of vulnerable components of wind turbine but also one of the most prone to failure components, so fault diagnosis and monitoring of the rolling bearing is the focus. Vibrational analysis is widely used for analysis of bearings. However, extraction of fault signatures from practical signals is always a great challenge. This paper proposes a new method for identifying incipient failures based on monitoring certain statistical parameters and a combination of the Hilbert and wavelet transforms. Then fault diagnosis system of wind turbine rolling bearing has been developed in LabVIEW 8.5 professional Edition. Experimental results have proved that the developed system can efficiently identify rolling bearing fault.

关键词： Rolling Bearing Characteristic parameter Hilbert Wavelet transform LabVIEW Fault Diagnosis

作者: ZHENG Xiaoxia XU Haosong

作者单位: School of Electric Power and Automation Engineering, Shanghai University of Electric Power, Shanghai 200090

会议类型: 国际会议

会议名称: The 31st Chinese Control Conference(第三十一届中国控制会议)

会议地点: 合肥

会议语种:英文

页码: 5290-5293

在线出版日期: 2012-07-01（万方平台首次上网日期，不代表论文的发表时间）