Acoustic emission source location based on artificial neural network

Yueqing Zhu; Chuanyi Tao; Xiaofeng Gao; Jingke Li; Wei Wang; Hao Wang; Jingnan Zhang; Yubing Liu

doi:10.1117/12.2612284

18 April 2022 Acoustic emission source location based on artificial neural network

Yueqing Zhu, Chuanyi Tao, Xiaofeng Gao, Jingke Li, Wei Wang, Hao Wang, Jingnan Zhang, Yubing Liu

Proceedings Volume 12047, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVI; 1204710 (2022) https://doi.org/10.1117/12.2612284
Event: SPIE Smart Structures + Nondestructive Evaluation, 2022, Long Beach, California, United States

Abstract

Acoustic emission (AE) is an effective technology that can be used for structural health monitoring. One of the most attractive features is the ability to locate AE sources. Characteristic parameters of waveform importing Artificial Neural Network (ANN) model is proposed for acoustic emission source location. The waveform of AE signal is apperceived by sensors, and decreases dispersion effect by wavelet transform. Input of ANN includes characteristic parameters of AE signal, waveform data and characteristic quantities which have been preprocessed. Time difference of signals and other parameters acts as sample which can decrease the influence of wave speed. Based on the agreement that ANN has the ability approximate any nonlinear mapping, it is feasible to build a model of time difference of signals and other characteristics with AE source position. This locating method can be widely used in AE source location on account of high accuracy, practicality and reliability.

Conference Presentation

Citation Download Citation

Yueqing Zhu, Chuanyi Tao, Xiaofeng Gao, Jingke Li, Wei Wang, Hao Wang, Jingnan Zhang, and Yubing Liu "Acoustic emission source location based on artificial neural network", Proc. SPIE 12047, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVI, 1204710 (18 April 2022); https://doi.org/10.1117/12.2612284

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