Fault Detection in Power Equipment via an Unmanned Aerial System Using Multi Modal Data
Fault Detection in Power Equipment via an Unmanned Aerial System Using Multi Modal Data
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DOI：
10.3390/s19133014
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发表时间：
2019-07-01
期刊：
影响因子：
3.9
通讯作者：
Berton, Andrea
中科院分区：
文献类型：
--
作者：
Jalil, Bushra;Leone, Giuseppe Riccardo;Berton, Andrea
输电线路通过变电站连接发电厂和用电点。最重要的是，对损坏的架空电线和生锈的导体进行评估对于公共安全极其重要；因此，必须定期检查电源线和相关组件，以确保持续供电并识别任何故障和缺陷。为了实现这些目标，近年来，无人机（UAV）得到了广泛的应用。事实上，它们提供了一种安全的方法，使传感器靠近输电线路及其相关组件，而无需在检查期间停止设备，并降低运营成本和风险。在这项工作中，一架配备多模态传感器的无人机捕获可见光和红外域的图像并将其传输到地面站。我们使用最先进的计算机视觉方法来突出显示电气基础设施的预期故障（即热点）或损坏的组件（即损坏的绝缘体）。红外成像对实际运行环境中的大范围和光照变化具有不变性，支持输电线路故障的识别；同时对神经网络进行调整和训练，以从光学视频流中检测和分类绝缘体。我们展示了我们对意大利帕尔马无人机捕获的数据的方法。
The power transmission lines are the link between power plants and the points of consumption, through substations. Most importantly, the assessment of damaged aerial power lines and rusted conductors is of extreme importance for public safety; hence, power lines and associated components must be periodically inspected to ensure a continuous supply and to identify any fault and defect. To achieve these objectives, recently, Unmanned Aerial Vehicles (UAVs) have been widely used; in fact, they provide a safe way to bring sensors close to the power transmission lines and their associated components without halting the equipment during the inspection, and reducing operational cost and risk. In this work, a drone, equipped with multi-modal sensors, captures images in the visible and infrared domain and transmits them to the ground station. We used state-of-the-art computer vision methods to highlight expected faults (i.e., hot spots) or damaged components of the electrical infrastructure (i.e., damaged insulators). Infrared imaging, which is invariant to large scale and illumination changes in the real operating environment, supported the identification of faults in power transmission lines; while a neural network is adapted and trained to detect and classify insulators from an optical video stream. We demonstrate our approach on data captured by a drone in Parma, Italy.