SILF Dataset: Fault Dataset for Solar Insecticidal Lamp Internet of Things node

Solar insecticidal lamps (SIL) are commonly used agricultural pest control devices that attract pests through a lure lamp and eliminate them using a high-voltage metal mesh. When integrated with Internet of Things (IoT) technology, SIL systems can collect various types of data, e.g., pest kill counts, meteorological conditions, soil moisture levels, and equipment status. However, the proper functioning of SIL-IoT is a prerequisite for enabling these capabilities. Therefore, this paper introduces the component composition and fault analysis of SIL-IoT. By examining long-term operational data from seven nodes deployed in real-world scenarios, different fault modes are identified. Six typical machine methods are adopted to verify the validity of the proposed dataset. The results indicate that machine learning algorithms can achieve high accuracy on the proposed dataset. Notably, voltage, current, and meteorological data play a crucial role in the fault diagnosis process for both SIL-IoT and other related agricultural IoT devices.

太阳能杀虫灯（Solar Insecticidal Lamps, SIL）是一类广泛应用的农业害虫防控装置，通过诱虫灯吸引害虫，并借助高压金属网将其灭杀。当与物联网（Internet of Things, IoT）技术结合后，SIL系统可采集多维度数据，例如害虫灭杀数量、气象状况、土壤湿度以及设备运行状态。然而，SIL-IoT系统的正常运行是实现上述数据采集功能的前提条件。因此，本文介绍了SIL-IoT的组件构成与故障分析内容。通过分析部署于真实场景的七个监测节点的长期运行数据，本研究识别出多种故障模式。本文采用六种典型机器学习方法验证了所提出数据集的有效性。实验结果表明，机器学习算法在该数据集上可取得较高的准确率。值得注意的是，电压、电流及气象数据在SIL-IoT及其他同类农业物联网设备的故障诊断流程中发挥着关键作用。