Acceleration sensor technology for rail track asset condition monitoring
| Autor: | Seppo J. Rantala, Klaus Känsälä, Pekka Leviäkangas, Osmo Kauppila |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
050210 logistics & transportation
Information technology/maintenance & inspection/railway systems 021103 operations research Data collection ta213 business.industry Computer science 05 social sciences Real-time computing 0211 other engineering and technologies Condition monitoring 02 engineering and technology Asset (computer security) Track (rail transport) General Business Management and Accounting Acceleration 0502 economics and business Global Positioning System Wireless Asset management Safety Risk Reliability and Quality business ta512 Civil and Structural Engineering |
| Zdroj: | Känsälä, K, Rantala, S, Kauppila, O & Leviäkangas, P 2018, ' Acceleration sensor technology for rail track asset condition monitoring ', Proceedings of Institution of Civil Engineers: Management, Procurement and Law, vol. 171, no. 1, pp. 32-40 . https://doi.org/10.1680/jmapl.17.00040 |
| DOI: | 10.1680/jmapl.17.00040 |
| Popis: | In the rail traffic industry, the utilisation of inexpensive real-time sensors and the industrial internet of things for proactive asset management is a relatively new concept with great potential. As railways are one of the longest-lasting infrastructure assets, even marginal efficiency and cost gains have a significant impact on the life-cycle cost. This paper shows how wireless three-dimensional acceleration sensor technology can be applied to monitor track condition. The data collection was carried out in October 2016 on a railway line operated by Finnish Railways. In the test, a sensor was attached to a train unit and the acceleration of the train on a track segment was repeatedly measured at variable speeds. The collected data set was enhanced using map-matching and Bayesian filtering in order to improve the Global Positioning System location accuracy of the data. The filtered acceleration signals were analysed, and detected anomalies were compared against known parameters such as bridges and switches. The results of the testing support the feasibility of the concept. Finally, the implications of the concept regarding proactive asset management of track networks and statistical process control-based monitoring of tracks’ condition are discussed. |
| Databáze: | OpenAIRE |
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