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

Doyeob Yeo; Ji-Hoon Bae; Jae Cheol Lee

Journal of The Korea Society of Computer and Information

Abbr : JKSCI
2019, 24(9), pp.21-27
Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science

Doyeob Yeo ¹, Ji-Hoon Bae ², Jae Cheol Lee ³

¹한국전자통신연구원
²대구가톨릭대학교
³한국원자력연구원

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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