AN IMPROVED HYBRID OF HILBERT AND WAVELET TRANSFORMS IN BEARING FAULT SIGNATURE EXTRACTION
Rolling element bearings are widely used in industry, thus the detection and diagnosis of bearing fault become important in order to avoid the interruption of production. However, extraction of fault signatures from a collected signal in a practical working environment is always a great challenge. This paper proposed an improved combination of Hilbert and wavelet transforms with the motivation to identify early bearing fault signatures. A traditional combination of Hilbert and wavelet transforms was employed for comparison study and an indicator to evaluate fault detection capability of methods was developed. Real rail vehicle bearing data were used to validate the proposed method. Analysis results show that the extraction capability of bearing fault signatures is greatly enhanced by the proposed method.
Rolling Element Bearings Hilbert Transform Wavelet Transform Fault Detection Fault Detection Capability Evaluation.
Dong Wang Qiang Miao Xianfeng Fan Hong-Zhong Huang
School of Mechatronics Engineering,University of Electronic Science and Technology of China,Chengdu, Sichuan, China, 610054
国际会议
北京
英文
1722-1729
2008-10-27（万方平台首次上网日期，不代表论文的发表时间）