A5007633255- Machine Fault Diagnosis Techniques
- Gear and Bearing Dynamics Analysis
- Fault Detection and Control Systems
- Engineering Diagnostics and Reliability
Central South University
2020-2021
Changsha Mining and Metallurgy Research Institute (China)
2021
For a rolling bearing fault that has nonlinearity and nonstationary characteristics, it is difficult to identify the category. A clustering diagnosis method based on ensemble empirical mode decomposition (EEMD), permutation entropy (PE), linear discriminant analysis (LDA), Gath–Geva (GG) algorithm proposed. Firstly, we decompose vibration signal using EEMD, several inherent modal components are obtained. Then, values of each component calculated get feature vector, vector reduced by LDA be...
Rolling bearing is an important part of mechanical equipment. Timely detection rolling fault one the factors to ensure safe operation In order diagnose accurately, a novel diagnosis method based on adaptive feature selection and clustering proposed. Firstly, vibration signal obtained from decomposed by ensemble empirical mode decomposition(EEMD) extract as much information possible. Feature extraction performed for each intrinsic function(IMF) component original signal, finally 240 features...