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ASME Press Select Proceedings

International Conference on Control Engineering and Mechanical Design (CEMD 2017)

Editor
Chao Li
Chao Li
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ISBN:
9780791861677
No. of Pages:
324
Publisher:
ASME Press
Publication date:
2018

The fault diagnosis of rolling element bearings (REB) has attracted substantial attention recently due to its importance for the bearing health management. The methods based on empirical mode decomposition and intelligent classification are widely used for REB fault diagnosis. However, there still exists two shortcomings in the fault diagnosis methods: 1) A large amount of redundant information is difficult to identify and delete; and 2) Aliasing patterns decreased the classification accuracy. To deal with these two shortcomings, an improved fault diagnosis method based on rough set and dependent feature vector (RS-DFV) is proposed in this paper. In RS-DFV method, the...

Introduction
The Proposition of RS-DFV
Summary
Acknowledgement
References
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