Open AccessResearch on feature extraction of steam turbine shafting vibration signal
Author(s) -
Qingliang Niu,
Yuli Gong,
Guohong Tian,
Deke Zhang,
Ming Zhang
Publication year - 2022
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 - 10.1088/1742-6596/2187/1/012073
Subject(s) - steam turbine , vibration , feature extraction , signal (programming language) , fault (geology) , wavelet , turbine , feature (linguistics) , engineering , condition monitoring , pattern recognition (psychology) , extraction (chemistry) , computer science , acoustics , artificial intelligence , mechanical engineering , physics , electrical engineering , geology , linguistics , philosophy , chemistry , chromatography , seismology , programming language
Vibration signal feature extraction is the primary problem of steam turbine condition monitoring and fault diagnosis. This paper focused on the analysis and recognition of vibration signal feature extraction, carried out model innovation on the basis of the existing status, and studied new modeling and solution methods. The vibration fault signals of typical steam turbine shafting were described respectively, and the time-frequency characteristics were studied by wavelet analysis