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Article

A Size-Controlled AFGAN Model for Ship Acoustic Fault Expansion

by 1,2,†, 3,*,†, 4,* and 3
1
Key Laboratory of High Performance Ship Technology, Wuhan University of Technology, Ministry of Education, Wuhan 430063, China
2
Key Laboratory of Marine Power Engineering & Technology, Wuhan University of Technology, Wuhan 430063, China
3
Key Laboratory of Modern Acoustics and Institute of Acoustics, Nanjing University, Nanjing 210000, China
4
Department of Mechanical Engineering, The University of Western Australia, Crawley 6008, Australia
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Appl. Sci. 2019, 9(11), 2292; https://doi.org/10.3390/app9112292
Received: 3 April 2019 / Revised: 29 May 2019 / Accepted: 29 May 2019 / Published: 3 June 2019
Identifying changes in the properties of acoustical sources based on a small number of sample data from measurements has been a challenge for decades. Typical problems are the increasing sound power from a vibrating source, decreasing transmission loss of a structure, and decreasing insertion loss of vibration mounts. Limited access to structural and acoustical data from complex acoustical systems makes it challenging to extract complete information of the system and, in practice, often only a small amount of test data is available for detecting changes. Although sample expansion via interpolation can be implemented based on the priori knowledge of the system, the size of the expanded samples also affects identification performance. In this paper, a generative adversarial network (GAN) is employed to expand the acoustic fault vibration signals, and an Acoustic Fault Generative Adversarial Network (AFGAN) model is proposed. Moreover, a size-controlled AFGAN is designed, which includes two sub-models: the generator sub-model generates expanded samples and also determines the optimal sample size based on the information entropy equivalence principle, while the discriminator sub-model outputs the probabilities of the input samples belonging to the real samples and provides the generator with information to guide sample size considerations. Some real data experiments have been conducted to verify the effectiveness of this method. View Full-Text
Keywords: acoustic fault identification; sample expansion; generative adversarial network; size control acoustic fault identification; sample expansion; generative adversarial network; size control
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MDPI and ACS Style

Zhang, L.; Wei, N.; Du, X.; Wang, S. A Size-Controlled AFGAN Model for Ship Acoustic Fault Expansion. Appl. Sci. 2019, 9, 2292. https://doi.org/10.3390/app9112292

AMA Style

Zhang L, Wei N, Du X, Wang S. A Size-Controlled AFGAN Model for Ship Acoustic Fault Expansion. Applied Sciences. 2019; 9(11):2292. https://doi.org/10.3390/app9112292

Chicago/Turabian Style

Zhang, Linke, Na Wei, Xuhao Du, and Shuping Wang. 2019. "A Size-Controlled AFGAN Model for Ship Acoustic Fault Expansion" Applied Sciences 9, no. 11: 2292. https://doi.org/10.3390/app9112292

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