상세정보
학술지
Unsupervised Learning-Based Pipe Leak Detection using Deep Auto-Encoder
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- 저자
- 여도엽, 배지훈, 이재철
- 발행일
- 201909
- 출처
- 한국컴퓨터정보학회논문지, v.24 no.9, pp.21-27
- ISSN
- 1598-849X
- 출판사
- 한국컴퓨터정보학회
- 초록
- 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.
- KSP 제안 키워드
- Acoustic data, Auto-Encoder(AE), Data collected, Free Data, Free state, Image feature, Learning-based, PLD method, Sensor node, Time series, Unsupervised feature extraction