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首页> 外文期刊>IFAC PapersOnLine >Neural networks as a diagnosing tool for industrial level measurement through non-contacting radar type and support to the decision for its better application
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Neural networks as a diagnosing tool for industrial level measurement through non-contacting radar type and support to the decision for its better application

机译：神经网络作为通过非接触式雷达类型进行工业液位测量的诊断工具，并为更好地应用做出支持

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Abstract: The aim of this study was to develop an analysis tool based on artificial neural networks (ANN) to detect level measurement problems with free wave propagation radars. The trend of using this type of radar has been growing in the last ten years mainly because of its easy installation on the top of tanks and reservoirs, and for its low rate maintenance comparing to other level measurement technologies. For the experiments, a Rosemount radar was used and the training of the neural network was based on the data from the software Radar Master. Therefore, some network topologies in different scenarios were tested and it was possible to demonstrate the efficiency of the ANN with accuracy rate between 94.44 to 100% for the first experiment with networks using 10, 20 or 50 neurons in the hidden layer. This technique was applied in a real industrial application, a sugar and ethanol mill, and accuracy rate was about 87,0 to 96,1%. This methodology can be applied to asset management software for diagnosis report or troubleshooting which would increase the level measurement reliability and plant safety.
机译：摘要：本研究的目的是开发一种基于人工神经网络（ANN）的分析工具，以检测自由波传播雷达的物位测量问题。在过去的十年中，使用这种类型的雷达的趋势一直在增长，这主要是由于其易于安装在储罐和水箱顶部以及与其他液位测量技术相比维护成本低。对于实验，使用了罗斯蒙特雷达，并且基于Radar Master软件的数据对神经网络进行了训练。因此，在不同情况下测试了一些网络拓扑，并且有可能证明ANN的效率，在第一个实验中，在隐藏层中使用10、20或50个神经元的网络的准确率在94.44至100％之间。该技术已在实际的工业应用中（糖和乙醇工厂）应用，准确率约为87.0至96,1％。该方法可以应用于资产管理软件以进行诊断报告或故障排除，这将提高液位测量的可靠性和工厂安全性。

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