Analysis and identification for the electromagnetic ultrasonic interfacial echoes using instantaneous spectrum and artificial neural network  

作　　者：SONG Weihua WANG Xiaomin LI Mingxuan 

机构地区：[1]Institute of Acoustics, The Chinese Academy of Sciences Beijing 100080 [2]~raduate School of the Chinese Academy of Sciences Beijing 100029

出　　处：《Chinese Journal of Acoustics》2008年第3期222-230,共9页声学学报（英文版）

基　　金：the National Natural Science Foundation of China(10234060,10574138,10474113)

摘　　要：The proper frequency is experimentally chosen to be the operation frequency of the electromagnetic acoustic transducer. The instantaneous amplitude, phase and frequency of the detected ultrasonic echoes from a multilayer adhesive sample of steel and rubber materials are calculated and composed to form three-dimensional instantaneous spectrum which is successful to distinguish the testing signals from different adhesive states qualitatively. Then, average instantaneous parameters in sensitive time window are picked up and used as the input eigenvectors for the BP artificial neural network. Identified results in both training and testing volumes demonstrate that the detected electromagnetic ultrasonic interracial echoes can be identified and classified automatically with the correctness ratio larger than 95%.The proper frequency is experimentally chosen to be the operation frequency of the electromagnetic acoustic transducer. The instantaneous amplitude, phase and frequency of the detected ultrasonic echoes from a multilayer adhesive sample of steel and rubber materials are calculated and composed to form three-dimensional instantaneous spectrum which is successful to distinguish the testing signals from different adhesive states qualitatively. Then, average instantaneous parameters in sensitive time window are picked up and used as the input eigenvectors for the BP artificial neural network. Identified results in both training and testing volumes demonstrate that the detected electromagnetic ultrasonic interracial echoes can be identified and classified automatically with the correctness ratio larger than 95%.

分 类 号：O422[理学—声学]