论文标题
通过在线词典学习中的大型水网络中的故障处理
Fault Handling in Large Water Networks with Online Dictionary Learning
论文作者
论文摘要
由于其模型的数学复杂性并通过传感器放置增加了数据可用性，因此水分配网络中的故障检测和隔离是一个主题。在这里，我们通过提供数据驱动的替代方案来简化模型，该替代方案在执行传感器放置时考虑了网络拓扑，然后根据传感器数据继续通过在线词典学习构建网络模型。在线学习很快，可以解决大型网络，因为它一次处理一小部分信号，并且可以将新数据连续集成到现有网络模型中，无论是在遇到新数据示例时还是在培训中进行培训，也可以在生产中进行。在小型和大规模网络上测试时，该算法表现出良好的性能。
Fault detection and isolation in water distribution networks is an active topic due to its model's mathematical complexity and increased data availability through sensor placement. Here we simplify the model by offering a data driven alternative that takes the network topology into account when performing sensor placement and then proceeds to build a network model through online dictionary learning based on the incoming sensor data. Online learning is fast and allows tackling large networks as it processes small batches of signals at a time and has the benefit of continuous integration of new data into the existing network model, be it in the beginning for training or in production when new data samples are encountered. The algorithms show good performance when tested on both small and large-scale networks.
