Unsupervised Learning-Based Pipe Leak Detection using Deep Auto-Encoder (음향센서로부터 수집된 데이터를 이용한 비지도 학습 기반의 플랜트 배관계에 대한 미세누출 탐지 방법)

Doyeob Yeo (여도엽); Ji-Hoon Bae (배지훈); Jae Cheol Lee (이재철)

doi:10.9708/jksci.2019.24.09.021

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.

KEYWORDS

Pipe leak detection, Acoustic signal, Unsupervised learning, Auto-encoder, Deep learning

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* References for papers published after 2023 are currently being built.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

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

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Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

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

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KEYWORDS

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REFERENCES (15) * References for papers published after 2023 are currently being built.

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* References for papers published after 2023 are currently being built.