Open AccessFault Diagnosis Technology of Rolling Bearing in Space Station
Author(s) -
Daren Yu,
Wenbo Wu,
Hongbo Fu,
Yang Wang,
Ke Wang
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1325/1/012015
Subject(s) - bearing (navigation) , frequency domain , fault (geology) , feature vector , vibration , time–frequency analysis , computer science , entropy (arrow of time) , time domain , pattern recognition (psychology) , engineering , feature (linguistics) , artificial intelligence , acoustics , computer vision , linguistics , philosophy , physics , filter (signal processing) , quantum mechanics , seismology , geology
With the development of space station engineering, rolling bearings have been widely used in space station. Whether the bearing is healthy or not is related to the safety of the experimental facilities. Fault diagnosis of rolling bearings includes time domain analysis and frequency domain analysis. By analyzing bearing fault information, time-frequency information entropy is more representative as fault feature. Firstly, the vibration signal of acceleration sensor is decomposed by EEMD method, and the intrinsic mode function is obtained. The time-frequency information is obtained by Fourier transform, and the information entropy of the time-frequency information is calculated. Then, the fault feature is self-adaptive dimensionality reduction. Finally, the fault feature is trained by using support vector machine, and the data is tested. The experimental results show that the method can diagnose bearing fault with high accuracy.