Bearing Fault Diagnosis Using Reconstruction Adaptive Determinate Stationary Subspace Filtering and Enhanced Third-Order Spectrum
Author:
Affiliation:
1. Maritime College, Guangdong Ocean University, Zhanjiang, China
2. Graduate School of Bioresources, Mie University, Tsu, Mie, Japan
3. School of Mechanical Electronic & Information Engineering, Huangshan University, Huangshan, China
Funder
Program for Scientific Research Start-Up Funds of Guangdong Ocean University
Key Research and Development Project from Anhui Province of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/7361/9785401/09759381.pdf?arnumber=9759381
Reference25 articles.
1. Adaptive variational mode decomposition and its application to multi-fault detection using mechanical vibration signals
2. Fault diagnosis of rolling bearing based on empirical mode decomposition and improved manhattan distance in symmetrized dot pattern image
3. Time–frequency analysis based on ensemble local mean decomposition and fast kurtogram for rotating machinery fault diagnosis
4. An Enhanced Intrinsic Time-Scale Decomposition Method Based on Adaptive Lévy Noise and Its Application in Bearing Fault Diagnosis
5. Independence-oriented VMD to identify fault feature for wheel set bearing fault diagnosis of high speed locomotive
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3. Bearing Fault Feature Enhancement and Diagnosis Based on Savitzky–Golay Filtering Gramian Angular Field;IEEE Access;2024
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