DEFECT IDENTIFICATION BY AN ULTRASONIC CYLINDRICAL

摘要：

In this paper,a new method is presented for defects classification by ultrasonic cylindrical phased array.Firstly,a finite element model is conducted to simulate the defects identification by the cylindrical phased array transducer.A series of simulation are done for 4 types of defects with different sizes by a 64-element cylindrical phased transducer with the center frequency of 500 kHz.Then,the Wavelet-packet transform decompose algorithm is used to four-levers decompose,reconstruct and extract the feature of these echo signals.Finally,the reconstructed signals are used to the deep neural network to the defect classification.The accuracy of the known defects classification is 100％,which means the method is feasible for classification by ultrasonic cylindrical phased array.

关键词： Ultrasonic cylindrical phased array Defect classification Wavelet-packet transform Deep neural network

作者: Shi-wen CHEN Bi-xing ZHANG

作者单位: State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing, 100190, China

会议类型: 国际会议

会议名称: The 2016 Symposium on Piezoelectricity,Acoustic Waves and Device Applications(2016全国压电和声波理论及器件技术研讨会)

会议地点: 西安

会议语种:英文

页码: 301-304

在线出版日期: 2016-10-21（万方平台首次上网日期，不代表论文的发表时间）