Research about online failure prediction of rail vehicle core components (such as wheels, bearings, and bogies) based on big data and artificial intelligence (AI) has become popular in view of its role of improving rail vehicle operation safety. The recent vibration energy harvesting sensor network relieves sensor nodes' dependence on wired power, which provides a green and low-cost way of collecting data from rail vehicle core components. However, the integration of an energy harvesting sensor network and AI to provide online failure prediction for rail vehicle components still faces several challenges, such as weak energy harvesting power and unstable vehicle-ground communication data rate. In this article, EH-Edge, an energy harvesting-driven cloud-edge-end device collaborative Internet of Things (IoT) platform, is proposed to efficiently integrate energy harvesting and AI to solve these challenges. A two-level collaborative AI failure prediction is proposed and deployed in the EH-Edge platform to reduce energy consumption in terms of sensor node, amount of data upload, and time delay of failure prediction. Detailed software and hardware designs and real-world data sets are also published.

中文翻译：

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EH-Edge-能量收集驱动的边缘IoT平台，用于轨道交通车辆的在线故障预测：以云，边缘和终端设备协作计算范例为例

鉴于其对提高轨道车辆运行安全性的作用，基于大数据和人工智能（AI）的轨道车辆核心部件（例如车轮，轴承和转向架）在线故障预测的研究已变得越来越流行。最近的振动能量收集传感器网络减轻了传感器节点对有线电源的依赖，这为从轨道车辆核心组件收集数据提供了一种绿色且低成本的方法。然而，将能量收集传感器网络和AI集成以提供铁路车辆组件的在线故障预测仍然面临着一些挑战，例如能量收集能力弱和车辆与地面通信数据速率不稳定。在本文中，EH-Edge是一种由能量收集驱动的云边缘终端设备协作式物联网（IoT）平台，提出了将能源收集和人工智能有效集成以解决这些挑战的建议。提出了两级协作式AI故障预测，并将其部署在EH-Edge平台中，以减少传感器节点，数据上传量和故障预测的时间延迟方面的能耗。详细的软件和硬件设计以及实际数据集也已发布。