Deep-Learning-Based Gas Leak Source Localization From Sparse Sensor Data
Deep-Learning-Based Gas Leak Source Localization From Sparse Sensor Data
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DOI：
10.1109/jsen.2022.3202134
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发表时间：
2022-11
影响因子：
4.3
通讯作者：
Diaa Badawi;I. Bassi;Ieee Sule Ozev Member;Ieee A. Enis Cetin Fellow
中科院分区：
文献类型：
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作者：
Diaa Badawi;I. Bassi;Ieee Sule Ozev Member;Ieee A. Enis Cetin Fellow
在本文中，我们解决了利用稀疏且不可靠的时空化学传感器数据估计气体泄漏源位置的问题。我们将估计潜在气体信号以及预测源位置的任务视为一个逆问题。为此，我们开发了一种基于深度学习投影的新框架。我们在深度模型的结构中融入了传统的凸集投影（POCS）迭代，以获得符合我们对气体浓度分布时空结构先验知识的正则化解决方案。我们使用离散余弦变换（DCT）层来模拟气体羽流信号的平滑特性。在DCT域中，我们将特征图投影到一个低通区域，其边界在训练过程中使用反向传播算法确定。此操作等同于投影到一个凸集上。此外，这些投影操作嵌入在卷积神经网络的非线性结构中。我们处理了两种不同类型的数据：来自工厂的甲烷 - 丙烷泄漏以及室内环境中的异丙醇（isopropanol）蒸汽泄漏。给出了实验结果。我们的结果表明，我们能够在获得潜在气体信号的平滑估计的同时，高精度地对源位置进行良好预测。
In this article, we address the problem of estimating the location of gas leak sources using sparse unreliable spatio-temporal chemical sensor data. We pose the task of estimating the underlying gas signal and predicting the source location as an inverse problem. For this purpose, we develop a novel deep-learning projection-based framework. We incorporate traditional projection-onto-convex-sets (POCS) iteration in the structure of the deep model to obtain a regularized solution that conforms to our prior knowledge of the spatio-temporal structure of the gas concentration distribution. We use discrete cosine transform (DCT) layers to model the smooth nature of the gas plume signal. In the DCT domain, we project the feature maps onto a low-pass region, whose boundary is determined during training using the backpropagation algorithm. This operation is equivalent to projecting onto a convex set. Furthermore, these projection operations are embedded in the nonlinear structure of a convolutional neural network. We address two different types of data: methane–propane leak from industrial plants and isopropyl alcohol (isopropanol) vapor leak in an indoor environment. Experimental results are presented. Our results show that we can obtain a smooth estimate of the underlying gas signal while obtaining a good source location prediction with high accuracy.