Modified Kernel Marginal Fisher Analysis for Feature Extraction and Its Application to Bearing Fault Diagnosis
The high-dimensional features of defective bearings usually include redundant and irrelevant information, which will degrade the diagnosis performance. Thus, it is critical to extract the sensitive low-dimensional characteristics for improving diagnosis performance. This paper proposes modified kern...
Saved in:
Main Authors: | Li Jiang, Shunsheng Guo |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2016-01-01
|
Series: | Shock and Vibration |
Online Access: | http://dx.doi.org/10.1155/2016/1205868 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Semisupervised Kernel Marginal Fisher Analysis for Face Recognition
by: Ziqiang Wang, et al.
Published: (2013-01-01) -
An Enhancement Deep Feature Extraction Method for Bearing Fault Diagnosis Based on Kernel Function and Autoencoder
by: Fengtao Wang, et al.
Published: (2018-01-01) -
Feature Extraction Strategy with Improved Permutation Entropy and Its Application in Fault Diagnosis of Bearings
by: Fan Jiang, et al.
Published: (2018-01-01) -
Multilevel Feature Extraction Method for Adaptive Fault Diagnosis of Railway Axle Box Bearing
by: Zhigang Liu, et al.
Published: (2023-01-01) -
Adaptive Morphological Feature Extraction and Support Vector Regressive Classification for Bearing Fault Diagnosis
by: Jun Shuai, et al.
Published: (2017-01-01)