| Issue |
E3S Web Conf.
Volume 252, 2021
2021 International Conference on Power Grid System and Green Energy (PGSGE 2021)
|
|
|---|---|---|
| Article Number | 02023 | |
| Number of page(s) | 4 | |
| Section | Research and Development of Electrical Equipment and Energy Nuclear Power Devices | |
| DOI | https://doi.org/10.1051/e3sconf/202125202023 | |
| Published online | 23 April 2021 | |
Investigation on recognition method of acoustic emission signal of the compressor valve based on CNN and LSTM network
1 Henan Key Lab of Information-Based Electrical Appliances, Zhengzhou University of Light Industry, Zhengzhou, Henan, 450000, China
2 School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan, 450000, China
3 School of Energy and Power Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan, 450000, China
* Corresponding author’s e-mail: wangtao@zzuli.edu.cn
The valve is one of the important parts of the reciprocating compressor, which directly affects the thermodynamic process and reliability of the compressor. In this paper, acoustic emission (AE) technology is used to predict the dynamic characteristics of valves. The AE signal of the compressor valve is analyzed based on the deep learning method, and the mapping relation between the AE signal and the dynamic characteristics of the valve is obtained. The results show that the prediction accuracy of the models trained by Long Short-Term Memory (LSTM) artificial neural network and Convolutional Neural Network (CNN) is 97% and 95%, respectively, which can accurately predict the dynamic characteristics of the valve. Although the prediction results of CNN are slightly lower than that of LSTM network, the calculation speed of CNN is relatively faster.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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