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

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2019, 24(9), pp.21-27
  • DOI : 10.9708/jksci.2019.24.09.021
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : August 14, 2019
  • Accepted : September 4, 2019
  • Published : September 30, 2019

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

1한국전자통신연구원
2대구가톨릭대학교
3한국원자력연구원

Accredited

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.

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