CRACK LOCALIZATION IN HYDRAULIC TURBINE BLADES BASED ON KERNEL INDEPENDENT COMPONENT ANALYSIS AND WAVELET NEURAL NETWORK

DOI

10.1080/18756891.2013.817065How to use a DOI?

Keywords

Crack localization, acoustic emission (AE), kernel independent component analysis (KICA), scaled conjugate gradient algorithm, wavelet neural network (WNN)

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.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Xianghong Wang
AU  - Hanling Mao
AU  - Hongwei Hu
AU  - Zhiyong Zhang
PY  - 2013
DA  - 2013/11/01
TI  - CRACK LOCALIZATION IN HYDRAULIC TURBINE BLADES BASED ON KERNEL INDEPENDENT COMPONENT ANALYSIS AND WAVELET NEURAL NETWORK
JO  - International Journal of Computational Intelligence Systems
SP  - 1116
EP  - 1124
VL  - 6
IS  - 6
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2013.817065
DO  - 10.1080/18756891.2013.817065
ID  - Wang2013
ER  -