Open AccessA study on the extraction and classification of bearing features
Author(s) -
Ying Xu
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/632/5/052083
Subject(s) - aliasing , bearing (navigation) , vibration , noise (video) , fault (geology) , computer science , modal , signal (programming language) , mode (computer interface) , pattern recognition (psychology) , artificial intelligence , algorithm , acoustics , materials science , physics , seismology , undersampling , polymer chemistry , image (mathematics) , programming language , geology , operating system
Bearings are the key components in rotating machinery equipment. The probability of failure occurring during use is relatively large. Bearings play an important role in the operation of the entire equipment. Therefore, it is of great significance to study bearing condition monitoring and fault diagnosis. This paper introduces the variational mode decomposition (VMD) method from the aspects of frequency separation, over-segmentation, initial convergence, and multi-modality of non-stationary signals, and analyzes the decomposition effect of VMD method on non-stationary signals through simulated signals The experiment proves that for non-stationary signals, the VMD algorithm can complete frequency separation well and overcome modal aliasing. Therefore, this paper uses the variational mode decomposition (VMD) method to study the bearing component failure, focusing on the analysis of the vibration signal of the rolling bearing failure under the background of strong noise.