A Size-Controlled AFGAN Model for Ship Acoustic Fault Expansion

• Published in: Applied sciences Vol. 9; no. 11; p. 2292
• Main Authors: Zhang, Linke, Wei, Na, Du, Xuhao, Wang, Shuping
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2019
• Subjects: acoustic fault identification; Acoustic properties; Acoustics; Architectural engineering; Change detection; Classification; Entropy (Information theory); generative adversarial network; Identification; Information processing; Information systems; Insertion loss; International conferences; Interpolation; Laboratories; Methods; Noise; Principal components analysis; Research methodology; Researchers; sample expansion; Ships; size control; Time series; Transmission loss; Vibration; Canada; Montreal Quebec Canada; United States > US; China
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app9112292

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.