ECOVIBE: On-Demand Sensing for Railway Bridge Structural Health Monitoring
2019 (English)In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 6, no 1, p. 1068-1078, article id 8445576Article in journal (Refereed) Published
Abstract [en]
Energy efficient sensing is one of the main objectives in the design of networked embedded monitoring systems. However, existing approaches such as duty cycling and ambient energy harvesting face challenges in railway bridge health monitoring applications due to the unpredictability of train passages and insufficient ambient energy around bridges. This paper presents ECOVIBE (Eco-friendly Vibration), an on-demand sensing system that automatically turns on itself when a train passes on the bridge and adaptively powers itself off after finishing all tasks. After that, it goes into an inactive state with near-zero power dissipation. ECOVIBE achieves these by: Firstly, a novel, fully passive event detection circuit to continuously detect passing trains without consuming any energy. Secondly, combining train-induced vibration energy harvesting with a transistor-based load switch, a tiny amount of energy is sufficient to keep ECOVIBE active for a long time. Thirdly, a passive adaptive off control circuit is introduced to quickly switch off ECOVIBE. Also this circuit does not consume any energy during inactivity periods. We present the prototype implementation of the proposed system using commercially available components and evaluate its performance in real-world scenarios. Our results show that ECOVIBE is effective in railway bridge health monitoring applications.
Place, publisher, year, edition, pages
2019. Vol. 6, no 1, p. 1068-1078, article id 8445576
Keywords [en]
Bridge circuits, Event detection, Internet of Things, Monitoring, on-demand sensing, Sensors, smart transportation., structural health monitoring, Structural panels, vibration energy harvesting, Vibrations, Embedded systems, Energy efficiency, Energy harvesting, Railroad bridges, Railroads, On demands, Smart transportations
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-35159DOI: 10.1109/JIOT.2018.2867086Scopus ID: 2-s2.0-85052713646OAI: oai:DiVA.org:ri-35159DiVA, id: diva2:1247243
2018-09-112018-09-112023-06-08Bibliographically approved