Lawal Omobolaji, Veluthedath Shajihan Shaik Althaf, Mechitov Kirill, Spencer Billie F
Department of Civil and Environmental Engineering, University of Illinois, 205 N. Matthews Ave, Urbana, IL 61801, USA.
Sensors (Basel). 2024 Aug 30;24(17):5633. doi: 10.3390/s24175633.
Of the 100,000 railroad bridges in the United States, 50% are over 100 years old. Many of these bridges do not meet the minimum vertical clearance standards, making them susceptible to impact from over-height vehicles. The impact can cause structural damage and unwanted disruption to railroad bridge services; rapid notification of the railroad authorities is crucial to ensure that the bridges are safe for continued use and to affect timely repairs. Therefore, researchers have developed approaches to identify these impacts on railroad bridges. Some recent approaches use machine learning to more effectively identify impacts from the sensor data. Typically, the collected sensor data are transmitted to a central location for processing. However, the challenge with this centralized approach is that the transfer of data to a central location can take considerable time, which is undesirable for time-sensitive events, like impact detection, that require a rapid assessment and response to potential damage. To address the challenges posed by the centralized approach, this study develops a framework for edge implementation of machine-learning predictions on wireless smart sensors. Wireless sensors are used because of their ease of installation and lower costs compared to their wired counterparts. The framework is implemented on the Xnode wireless smart sensor platform, thus bringing artificial intelligence models directly to the sensor nodes and eliminating the need to transfer data to a central location for processing. This framework is demonstrated using data obtained from events on a railroad bridge near Chicago; results illustrate the efficacy of the proposed edge computing framework for such time-sensitive structural health monitoring applications.
在美国的10万座铁路桥梁中，50%的桥梁已有100多年历史。其中许多桥梁不符合最低垂直净空标准，容易受到超高车辆的撞击。这种撞击可能会导致结构损坏，并对铁路桥梁服务造成不必要的干扰；迅速通知铁路当局对于确保桥梁安全继续使用并及时进行维修至关重要。因此，研究人员已经开发出方法来识别对铁路桥梁的这些撞击。最近的一些方法使用机器学习来更有效地从传感器数据中识别撞击。通常，收集到的传感器数据会传输到一个中心位置进行处理。然而，这种集中式方法面临的挑战是，将数据传输到中心位置可能需要相当长的时间，这对于像撞击检测这样需要快速评估和对潜在损坏做出响应的时间敏感事件来说是不可取的。为了应对集中式方法带来的挑战，本研究开发了一个用于在无线智能传感器上进行机器学习预测的边缘实现框架。使用无线传感器是因为它们与有线传感器相比安装方便且成本较低。该框架在Xnode无线智能传感器平台上实现，从而将人工智能模型直接带到传感器节点，无需将数据传输到中心位置进行处理。使用从芝加哥附近一座铁路桥梁上的事件获得的数据对该框架进行了演示；结果说明了所提出的边缘计算框架在此类时间敏感的结构健康监测应用中的有效性。
