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- W2378717567 abstract "Acoustic emission(AE) technique was used to detect cracks and their locations in turbine blades.Turbine runner has a complex structure and cracks occurred on some special regions.The source location of cracks in turbine runner blades was researched using classfication and regression functions of support vector machines(SVM).The results show that the SVM technique has 100 percent of recognition rate in crack region,which is higher than back propagation neural network(BPNN),and that estimating precision of distance from source of cracks to welding seam is somewhat better than BPNN.As a result,SVM algorithm is a suitable source location method for complex structures,especially the situation with small sample." @default.
- W2378717567 created "2016-06-24" @default.
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- W2378717567 date "2009-01-01" @default.
- W2378717567 modified "2023-09-24" @default.
- W2378717567 title "Damage localization in turbine runner blades using support vector machines and Neural Networks" @default.
- W2378717567 hasPublicationYear "2009" @default.
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