Hybrid feature selection method for SVM classification and its application for fault diagnosis of wear and peeling in journal bearing with a little muddy water using long-term real data | Zendy

TU Yilin | Zendy; Tsuyoshi INOUE | Zendy; Shota Yabui | Zendy; Keiichi Katayama | Zendy; Shigeyuki Tomimatsu | Zendy

Open Access

Hybrid feature selection method for SVM classification and its application for fault diagnosis of wear and peeling in journal bearing with a little muddy water using long-term real data

Author(s) -

TU Yilin,

Tsuyoshi INOUE,

Shota Yabui,

Keiichi Katayama,

Shigeyuki Tomimatsu

Publication year - 2022

Publication title -

journal of low frequency noise, vibration and active control

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.419

H-Index - 25

eISSN - 2048-4046

pISSN - 1461-3484

DOI - 10.1177/14613484221118997

Subject(s) - bearing (navigation) , support vector machine , feature selection , fault (geology) , feature (linguistics) , artificial intelligence , pattern recognition (psychology) , vibration , selection (genetic algorithm) , engineering , rotor (electric) , computer science , roller bearing , term (time) , data mining , mechanical engineering , geology , acoustics , linguistics , philosophy , physics , seismology , quantum mechanics , lubrication

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