MALSTM-MCN Ensemble Learning-based Planetary Gearbox Fault Diagnosis method
Author:
Affiliation:
1. Seoul National University,Department of Mechanical Engineering,Seoul,Korea
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10625768/10626308/10626919.pdf?arnumber=10626919
Reference9 articles.
1. Autocorrelation-based time synchronous averaging for condition monitoring of planetary gearboxes in wind turbines
2. A positive energy residual (PER) based planetary gear fault detection method under variable speed conditions
3. Gearbox Fault Diagnosis Using a Deep Learning Model With Limited Data Sample
4. Virtualization and deep recognition for system fault classification
5. A health-adaptive time-scale representation (HTSR) embedded convolutional neural network for gearbox fault diagnostics
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