Name
Title | ||
|---|---|---|
A Novel Framework for Fault Diagnosis Using Kernel Partial Least Squares Based on an Optimal Preference Matrix. |
Abstract | ||
|---|---|---|
In the standard kernel partial least squares (KPLS), the mapped data in the feature space need to be centralized before extraction of new score vectors. However, each vector of the centralized variables is often uniformly distributed, and some original features that can reflect the contribution of each variable to fault diagnosis might be lost. As a result, it might lead to misleading interpretati... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TIE.2017.2668986 | IEEE Transactions on Industrial Electronics |
Keywords | Field | DocType |
Fault diagnosis,Feature extraction,Kernel,Covariance matrices,Aluminum,Fault detection,Production | Kernel (linear algebra),Particle swarm optimization,Data mining,Feature vector,Fault detection and isolation,Feature extraction,Covariance matrix,Engineering,Principal component analysis,Genetic algorithm | Journal |
Volume | Issue | ISSN |
64 | 5 | 0278-0046 |
Citations | PageRank | References |
1 | 0.35 | 10 |
Authors | ||
6 | ||