An accurate pipeline leak classification and location estimation method can help to control and reduce the damage to the environment when spills happen. Some of the current research on this topic rely on the direct analysis of target frequencies from the monitoring of sensors. However, this assumes that the frequencies may be known before hand and such analysis can be very cumbersome. In this article, we propose convolutional neural network-based classification and location estimation methods, which use raw data instead of prefiltered (or preconditioned) information. The design approach is fully described and the network structure is discussed. Finally, analysis of experimental results validate the proposed network demonstrating that the classification and location estimation can be done with good accuracy.

中文翻译：

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卷积神经网络的管道泄漏分类定位系统

准确的管道泄漏分类和位置估计方法可以帮助控制和减少泄漏发生时对环境的破坏。当前关于该主题的一些研究依赖于对传感器监控的目标频率的直接分析。然而，这假设频率可能是事先已知的，并且这样的分析可能非常麻烦。在本文中，我们提出了基于卷积神经网络的分类和位置估计方法，该方法使用原始数据而不是经过预过滤（或预处理）的信息。完整描述了设计方法，并讨论了网络结构。最后，对实验结果的分析验证了所提出的网络，表明分类和位置估计可以很好地完成。