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Civil-Comp Proceedings
ISSN 1759-3433
CCP: 110
Edited by: J. Pombo
Paper 166

Strain Cycles Monitoring of Metallic Railway Bridges using a Wireless Sensor Network

G. Feltrin1, N. Popovic1, K.-E. Jalsan1 and M. Wojtera2

1Empa - Swiss Federal Laboratories for Materials Science and Technology, Switzerland
2Department of Microelectronics and Computer Science, Technical University of Lodz, Poland

Full Bibliographic Reference for this paper
G. Feltrin, N. Popovic, K.-E. Jalsan, M. Wojtera, "Strain Cycles Monitoring of Metallic Railway Bridges using a Wireless Sensor Network", in J. Pombo, (Editor), "Proceedings of the Third International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Stirlingshire, UK, Paper 166, 2016. doi:10.4203/ccp.110.166
Keywords: metallic railway bridges, fatigue assessment, strain cycles monitoring, wireless sensor network, sentinel nodes, event driven monitoring, embedded data processing.

As a result of the increasing traffic volume on the European railway network a major concern is the remaining fatigue life of old metallic bridges. Past investigations demonstrated that monitoring enabled more reliable fatigue assessments. In this paper, an event driven monitoring system based on a wireless sensor network that consisted of two functionally different components was designed and tested. Sentinel nodes detected approaching trains and alert monitoring nodes which were mounted on the bridge. After receiving the alarming message, these nodes started strain data recording and went back to power saving mode after completion. An embedded data processing algorithm transformed the recorded raw data into a much smaller data set representing the recorded strain cycles. This data was saved to a base station which transferred the data to a server using the cellular phone network. The test deployment on a railway bridge demonstrated that the monitoring system performed very reliably. The quality of the recorded data was very good. The combination of event driven monitoring and embedded data processing ensured a battery lifetime of five to six months.

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