Rolling Bearing Fault Diagnosis Using Modified LFDA and EMD With Sensitive Feature Selection

Rolling Bearing Fault Diagnosis Using Modified LFDA and EMD With Sensitive Feature Selection

In order to improve the accuracy of bearings fault diagnosis, one of the most crucial components of rotating machinery, a novel features extraction procedure incorporating an improved features dimensionality reduction method is proposed. In the first step, using the empirical mode decomposition meth...

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Bibliographic Details
Main Authors: Xiao Yu, Fei Dong, Enjie Ding, Shoupeng Wu, Chunyang Fan
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Fault diagnosis
features extraction
features reduction
sensitive features
Online Access:https://ieeexplore.ieee.org/document/8116607/

Internet

https://ieeexplore.ieee.org/document/8116607/

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