Extraction of the Early Fault Feature of Rolling Bearing based on MED-RSSD
During the early failure of the rolling bearing,the fault signal is very weak and the fault information is difficult to be extracted because of the influence of the background noise,in order to be able to effectively detect the bearing failure. A new diagnosis approach which combines minimum entropy...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Transmission
2018-01-01
|
| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2018.06.025 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | During the early failure of the rolling bearing,the fault signal is very weak and the fault information is difficult to be extracted because of the influence of the background noise,in order to be able to effectively detect the bearing failure. A new diagnosis approach which combines minimum entropy deconvolution（MED） and resonance sparse signal decomposition（RSSD） is put forward. Firstly,the MED is used to reduce noise signal of the bearing fault vibration signal. Then the RSSD is used to decompose the signal into the high resonance component containing the harmonic signal and the low resonance component containing the transient impact signal. Finally,the envelope power spectrum is used to extract the fault characteristic frequency from the low resonant component. Through simulation and experiment,this method is suitable for weak fault feature extraction. |
|---|---|
| ISSN: | 1004-2539 |