Deep-Learning-Based Pipe Leak Detection Using Image-Based Leak Features

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.