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Conference Paper
Deep-Learning-Based Pipe Leak Detection Using Image-Based Leak Features
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- Authors
- Ji-Hoon Bae, Doyeob Yeo, Doo-Byung Yoon, Se Won OH, Gwan Joong Kim, Nae-Soo Kim, Cheol-Sig Pyo
- Issue Date
- 2018-10
- Citation
- International Conference on Image Processing (ICIP) 2018, pp.2361-2365
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- In this paper, we propose a deep-learning-based pipe leak detection (PLD) technique using trajectory-based image features extracted from time-series acoustic data received from microphone sensor nodes. We developed root-mean-square-pattern and frequency-pattern images by reflecting the leakage signal characteristics in the time and frequency domains and used them for ensemble learning with the help of state-of-the-art residual networks. The experimental results obtained using the measured data of leakage signals in a laboratory-scale nuclear power plant environment are presented to validate the effectiveness of the proposed method for PLD. The results show that the proposed image features suitable for convolutional neural network-based deep-learning can provide reliable PLD performance in terms of classification accuracy despite the machine-driven complex noise environment.
- KSP Keywords
- Acoustic data, Convolution neural network(CNN), Ensemble Learning, Image Features, Image-based, Laboratory-scale, Leak Detection, Learning-based, Network-based, Nuclear power plant, Residual Network