Name
Title | ||
|---|---|---|
Crack Localization In Hydraulic Turbine Blades Based On Kernel Independent Component Analysis And Wavelet Neural Network |
Abstract | ||
|---|---|---|
Hydraulic turbine runner has a complex structure, and traditional location methods can't meet its requirement. This paper describes a source location of cracks in turbine blades by combining kernel independent component analysis (KICA) with wavelet neural network (WNN). The research shows that the location accuracy of WNN combined with KICA feature extraction is the best comparing with the results of WNN and back propagation neural network (BPNN). The method decreases the dimension of input parameters and improves the accuracy of location as well. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1080/18756891.2013.817065 | INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS |
Keywords | Field | DocType |
Crack localization, acoustic emission (AE), kernel independent component analysis (KICA), scaled conjugate gradient algorithm, wavelet neural network (WNN) | Kernel-independent component analysis,Wavelet neural network,Hydraulic turbines,Pattern recognition,Turbine blade,Back propagation neural network,Feature extraction,Artificial intelligence,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
6 | 6 | 1875-6891 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xianghong Wang | 1 | 0 | 0.34 |
| HanLing Mao | 2 | 1 | 0.70 |
| Hongwei Hu | 3 | 0 | 0.34 |
| Zhiyong Zhang | 4 | 0 | 0.34 |