Fault diagnosis based on the quantification of the fault features in a rotary machine
Author:
Funder
Ministry of Trade, Industry & Energy (MOTIE), Korea Institute for Advancement of Technology
Publisher
Elsevier BV
Subject
Software
Reference42 articles.
1. Iterative generalized synchrosqueezing transform for fault diagnosis of wind turbine planetary gearbox under nonstationary conditions;Feng;Mech. Syst. Signal Process.,2015
2. Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation;Feng;Renew. Energy,2012
3. Time–frequency signal analysis for gearbox fault diagnosis using a generalized synchrosqueezing transform;Li;Mech. Syst. Signal Process.,2012
4. Fault feature extraction of rotating machinery using a reweighted complete ensemble empirical mode decomposition with adaptive noise and demodulation analysis;Wang;Mech. Syst. Signal Process.,2020
5. Optimal IMF selection and unknown fault feature extraction for rolling bearings with different defect modes;Yang;Measurement,2020
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