Automatic classification approach to weld defects based on PCA and SVM
To improve the accuracy of automatic defect classification, a novel algorithm has been developed based on the principal component analysis (PCA) and support vector machine (SVM) methods. The original defect data are transformed to principal component space by the PCA algorithm and then the optimal dataset is selected. Then, the SVM is used for defect classification. For estimating the actual classification accuracy of the proposed method in a concrete system, the bootstrap method is introduced. The experimental result demonstrates that the accuracy of the new method is 90.75%, which promotes the evaluation accuracy by 3.24% and 4.93% compared with the SVM and MLP-ANN, respectively. Furthermore, the new method takes less computing time than the MLP-ANN method.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
Document Type: Research Article
Publication date: October 1, 2013
More about this publication?
- Official Journal of The British Institute of Non-Destructive Testing - includes original research and devlopment papers, technical and scientific reviews and case studies in the fields of NDT and CM.
- Information for Authors
- Submit a Paper
- Subscribe to this Title
- Information for Advertisers
- Terms & Conditions
- Ingenta Connect is not responsible for the content or availability of external websites