Volume 6, Issue 6, December 2013, Pages 1116 - 1124
CRACK LOCALIZATION IN HYDRAULIC TURBINE BLADES BASED ON KERNEL INDEPENDENT COMPONENT ANALYSIS AND WAVELET NEURAL NETWORK
- Xianghong Wang, Hanling Mao, Hongwei Hu, Zhiyong Zhang
- Corresponding Author
- Xianghong Wang
Available Online 9 January 2017.
- https://doi.org/10.1080/18756891.2013.817065How to use a DOI?
- Crack localization, acoustic emission (AE), kernel independent component analysis (KICA), scaled conjugate gradient algorithm, wavelet neural network (WNN)
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - JOUR AU - Xianghong Wang AU - Hanling Mao AU - Hongwei Hu AU - Zhiyong Zhang PY - 2017 DA - 2017/01 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 - https://doi.org/10.1080/18756891.2013.817065 ID - Wang2017 ER -