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Abstract: The analysis of ultrasonic signals obtained during weld
inspection is dealt with, aiming to distinguish different flaws with
signal analysis and classification methods. The features of
radio-frequency signal and demodulated signal are described, and an
evaluation method of distance and identification index is given, which is
used to compare the possibility of being classified. The flaw signal
classification is presented on the combination of wavelet analysis and
neural network. Results obtain demonstrate the effectiveness of RBFN
than BPN in learning speed and generalization.
Key words: Ultrasonic inspection Radio-frequency signal Distance
between-and-within classes Flaw classification
CLC No: G441.7
国家自然科学基金资助项目(59975085). 选自2000年第一届国际机械工程学术会议, 20011026
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