Open AccessAn Intelligent Bearing Fault Diagnosis Method Based on SF-SVM
Author(s) -
Bao’an Qiu,
Ping Sun,
Lili Li
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1210/1/012004
Subject(s) - bearing (navigation) , support vector machine , vibration , reliability (semiconductor) , computer science , fault (geology) , artificial intelligence , pattern recognition (psychology) , feature (linguistics) , stability (learning theory) , data mining , machine learning , acoustics , power (physics) , linguistics , physics , philosophy , quantum mechanics , seismology , geology
Rolling bearing, as a key component of rotating machinery, its health status directly determines the stability and reliability of the whole machine. The research on its intelligent diagnosis method has important engineering value and academic significance. However, due to actual engineering conditions, the types of bearing failures and the amount of data are limited. Aiming at the difficulty of extracting and selecting bearing vibration features under limited sample constraints, this pa-per proposes an intelligent fault diagnosis method of SF-SVM. On the basis of the short-time Fourier change, the L2 regularized sparse filter is used to extract the unsupervised feature of the bearing vibration time-frequency map. After obtaining the typical features of the bearing, the support vector machine is used for diagnosis.