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http://dx.doi.org/10.9708/jksci.2019.24.09.021

Unsupervised Learning-Based Pipe Leak Detection using Deep Auto-Encoder  

Yeo, Doyeob (Electronics and Telecommunications Research Institute (ETRI))
Bae, Ji-Hoon (Daegu Catholic University)
Lee, Jae-Cheol (Korea Atomic Energy Research Institute)
Publication Information
Journal of the Korea Society of Computer and Information / v.24, no.9, 2019 , pp. 21-27 More about this Journal
Abstract
In this paper, we propose a deep auto-encoder-based pipe leak detection (PLD) technique from time-series acoustic data collected by microphone sensor nodes. The key idea of the proposed technique is to learn representative features of the leak-free state using leak-free time-series acoustic data and the deep auto-encoder. The proposed technique can be used to create a PLD model that detects leaks in the pipeline in an unsupervised learning manner. This means that we only use leak-free data without labeling while training the deep auto-encoder. In addition, when compared to the previous supervised learning-based PLD method that uses image features, this technique does not require complex preprocessing of time-series acoustic data owing to the unsupervised feature extraction scheme. The experimental results show that the proposed PLD method using the deep auto-encoder can provide reliable PLD accuracy even considering unsupervised learning-based feature extraction.
Keywords
Pipe leak detection; Acoustic signal; Unsupervised learning; Auto-encoder; Deep learning;
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