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首页> 外文期刊>Brazilian journal of chemical engineering >Detection and on-line prediction of leak magnitude in a gas pipeline using an acoustic method and neural network data processing
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Detection and on-line prediction of leak magnitude in a gas pipeline using an acoustic method and neural network data processing

机译：使用声学方法和神经网络数据处理的天然气管道泄漏量检测和在线预测

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Considering the importance of monitoring pipeline systems, this work presents the development of a technique to detect gas leakage in pipelines, based on an acoustic method, and on-line prediction of leak magnitude using artificial neural networks. On-line audible noises generated by leakage were obtained with a microphone installed in a 60 m long pipeline. The sound noises were decomposed into sounds of different frequencies: 1 kHz, 5 kHz and 9 kHz. The dynamics of these noises in time were used as input to the neural model in order to determine the occurrence and the leak magnitude. The results indicated the great potential of the technique and of the developed neural network models. For all on-line tests, the models showed 100% accuracy in leak detection, except for a small orifice (1 mm) under 4 kgf/cm2 of nominal pressure. Similarly, the neural network models could adequately predict the magnitude of the leakages.
机译：考虑到监视管道系统的重要性，这项工作提出了一种基于声学方法并使用人工神经网络在线预测泄漏量的技术来检测管道中的气体泄漏。通过安装在60 m长的管道中的麦克风获得由泄漏产生的在线可听噪声。声音噪声被分解为不同频率的声音：1 kHz，5 kHz和9 kHz。这些噪声随时间变化的动态用作神经模型的输入，以便确定发生情况和泄漏量。结果表明该技术和已开发的神经网络模型的巨大潜力。对于所有在线测试，这些模型均显示出100％的泄漏检测精度，除了在4 kgf / cm2的公称压力下有一个小孔（1 mm）以外。同样，神经网络模型可以充分预测泄漏量。

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