음향센서로부터 수집된 데이터를 이용한 비지도 학습 기반의 플랜트 배관계에 대한 미세누출 탐지 방법

• Published in: 한국컴퓨터정보학회논문지 Vol. 24; no. 9; pp. 21 - 27
• Main Authors: 여도엽(Doyeob Yeo), 배지훈(Ji-Hoon Bae), 이재철(Jae-Cheol Lee)
• Format: Journal Article
• Language: Korean
• Published: 한국컴퓨터정보학회 01.09.2019
• Subjects: 컴퓨터학; Deep learning; Pipe leak detection; Acoustic signal; Auto-encoder; Unsupervised learning
• ISSN: 1598-849X; 2383-9945
• DOI: 10.9708/jksci.2019.24.09.021

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. KCI Citation Count: 0