Rolling bearing fault detection based on local characteristic-scale decomposition and teager energy operator

摘要：

　　In this paper, a rolling bearing fault detection method based on Local Characteristic-scale Decomposition (LCD) and Teager Energy operator (TEO) is proposed.Vibration signals is related to the bearing fault.However, the vibration signal of rolling bearing is nonlinear and has multiple components, which makes it difficult to analyze the signals by using traditional method such as the fast Fourier transform (FFT).LCD, a recently developed signal decomposition method, is especially capable for dealing with the complex signal by decomposing it into several intrinsic scale components (ISC).Furthermore, to extract fault diagnosis of the components, we used TEO to demodulate each ISC.The energy of fault feature frequencies was extracted as fault vector.The result shows that the method successfully diagnoses bearing fault.

关键词： rolling bearing fault diagnosis local characteristic-scale decomposition teager energy operator

作者: Liu Hongmei Chen Yu

作者单位: School of Reliability and Systems Engineering, Beihang University, Beijing, China

会议类型: 国际会议

会议名称: The 28th International Conference on Vibroengineering (第28届国际振动工程会议)

会议地点: 北京

会议语种:英文

页码: 126-129

在线出版日期: 2017-10-19（万方平台首次上网日期，不代表论文的发表时间）