Kernel Local Linear Discriminate Method for Dimensionality Reduction and Its Application in Machinery Fault Diagnosis
Dimensionality reduction is a crucial task in machinery fault diagnosis. Recently, as a popular dimensional reduction technology, manifold learning has been successfully used in many fields. However, most of these technologies are not suitable for the task, because they are unsupervised in nature an...
Saved in:
| Main Authors: | Kunju Shi, Shulin Liu, Hongli Zhang, Bo Wang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
|
| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2014/283750 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Joint Sparse Local Linear Discriminant Analysis for Feature Dimensionality Reduction of Hyperspectral Images
by: Cong-Yin Cao, et al.
Published: (2024-11-01) -
A Lightweight Kernel Density Estimation and Adaptive Synthetic Sampling Method for Fault Diagnosis of Rotating Machinery with Imbalanced Data
by: Wenhao Lu, et al.
Published: (2024-12-01) -
Unsupervised Learning for Machinery Adaptive Fault Detection Using Wide-Deep Convolutional Autoencoder with Kernelized Attention Mechanism
by: Hao Yan, et al.
Published: (2024-12-01) -
A New Fault Diagnosis Method of Rotating Machinery
by: Chih-Hao Chen, et al.
Published: (2008-01-01) -
Concurrent Fault Diagnosis for Rotating Machinery Based on Vibration Sensors
by: Qing-Hua Zhang, et al.
Published: (2013-04-01)