@inproceedings{8b46dab2dd224f42b2ef952852299e73,
title = "Design of a defect detection method based on shock-waveform decomposition and back propagation neural network",
abstract = "Cantilever beams with defects in different locations are studied in the finite element software. The acceleration response of the cantilever beams is processed by the shock-waveform decomposition method in which the characteristic parameters of the acceleration are extracted and the dataset is formed. Then, the dataset is trained by BP (back propagation) neural network to identify the location of defect. It is shown that the defect detection method based on the shock-waveform decomposition method and BP neural network has high defect detection accuracy and efficiency.",
keywords = "BPneural network, defect detection, shock-waveform decomposition",
author = "Guihong Liu and Qingming Li",
note = "Publisher Copyright: {\textcopyright} 2023 SPIE.; 2023 International Conference on Computer, Artificial Intelligence, and Control Engineering, CAICE 2023 ; Conference date: 17-02-2023 Through 19-02-2023",
year = "2023",
doi = "10.1117/12.2680795",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Xin Feng and Aniruddha Bhattacharjya",
booktitle = "International Conference on Computer, Artificial Intelligence, and Control Engineering, CAICE 2023",
address = "United States",
}