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About Fault Diagnosis Method of Rolling Bearing Based On Deep Belief Network
This document proposes a fault diagnosis method for rolling bearings using a deep belief network (DBN). It aims to address limitations of traditional methods by directly extracting features from vibration signals using the DBN's powerful feature extraction abilities. The method stacks multiple restricted Boltzmann machines (RBMs) to form a deep network that can automatically learn complex relationships between signals and machine health status. The results show this DBN-based method achieves higher diagnosis accuracy than existing techniques.
Detailed Information
| Author: | ['Chethan Gowda'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 7 |
| Language: | English |
| Format: | |
| Price: | FREE |
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