Name
Title | ||
|---|---|---|
Kernel Function and Parameters Optimization in KICA for Rolling Bearing Fault Diagnosis. |
Abstract | ||
|---|---|---|
Kernel independent component analysis (KICA) is a blind signal separation method which has a good effect for the treatment of non-linear signal. For introducing kernel techniques, the choices of kernel function and its kernel parameter have a great influence on the analytic results. A kernel function and its parameters optimization method is proposed on the basis of the similarity of source fault signals and kernel independent component. The similarity parameter is proposed to verify the merits or defects of KICA by using different kernel function and parameters. The simulation studies are processed, and the simulation conclusion is verified by the actual diagnostic case. These provide guidance for the application of the KICA method in the mechanical fault diagnosis. © 2013 ACADEMY PUBLISHER. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.4304/jnw.8.8.1913-1919 | JNW |
Keywords | Field | DocType |
fault diagnosis,kernel function,kernel parameters,kica,rolling bearing | Kernel (linear algebra),Mathematical optimization,Kernel smoother,Radial basis function kernel,Computer science,Algorithm,Kernel principal component analysis,Kernel method,Variable kernel density estimation,Blind signal separation,Distributed computing,Kernel (statistics) | Journal |
Volume | Issue | Citations |
8 | 8 | 0 |
PageRank | References | Authors |
0.34 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lingli Jiang | 1 | 6 | 2.04 |
| Bo Zeng | 2 | 76 | 13.74 |
| Francis R. Jordan | 3 | 0 | 0.34 |
| Anhua Chen | 4 | 4 | 2.48 |