Acoustic sources localization for composite pate using arrival time and BP neural network

• Published in: Polymer testing Vol. 115; p. 107754
• Main Authors: Hao, Wenfeng, Huang, Yingqi, Zhao, Guoqi
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2022; Elsevier
• Subjects: Acoustic sources localization; BP neural Network; Composite pate; Scanning laser Doppler vibrometer; Wave field; Composite pate; Acoustic sources localization; BP neural Network; Scanning laser Doppler vibrometer; Wave field
• ISSN: 0142-9418; 1873-2348
• DOI: 10.1016/j.polymertesting.2022.107754

This paper presents a machine learning approach to localizing a five-peak narrow-band modulated sinusoidal signal excitation source within a composite panel. In particular, the Back Propagation (BP) neural network is used. The idea is to use the arrival time of the first wave packet in a five-peak wave signal to locate their source. Specifically, this paper divides the composite material board into multiple regions, designs 8 receiving points to receive the signal from the excitation source, and finds the region where each source is located. The COMSOL numerical simulation platform is used to build a composite plate model and simulate the propagation of five-peak waves to train and test the machine learning network. Correspondingly, carry out experimental verification and use a scanning laser Doppler vibrometer (SLDV) to build a non-contact experimental platform to obtain the wave field information in the composite material plate. The results show that BP neural networks can learn to map signal features to their sources in both contexts. •The arrival time was used to locate acoustic source based on BP neural network.•The COMSOL numerical simulation platform is used to build composite plate model.•The propagation of five-peak waves was used to train and test the machine learning network.•A SLDV is used to obtain the wavefield information in composite plat.•BP neural networks can learn to map signal features to their sources.