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J.F. Guo, W.D. Wang, J.Z. Liu, "Online Measuring of the Wheel and Track Interactional Force Indirectly based on an Extreme Learning Machine Approach", in J. Pombo, (Editor), "Proceedings of the Second International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Stirlingshire, UK, Paper 90, 2014. doi:10.4203/ccp.104.90

Keywords: wheel, track, interactional force, vehicle acceleration, track geometry, extreme learning machine, back propagation.

Summary

Wheel and track interactional forces can be used to detect track status and guide track maintenance. This paper proposes a new indirect online method to detect wheel and track interactional forces based on an extreme learning machine (ELM) approach using acceleration and track geometry data. This method is different from the traditional methods. The ELM algorithm provides a good generalization in a few minutes with a very fast training speed, so it may be used online. The prediction model for wheel and track interactional forces has been validated by the use of field test data from the high speed comprehensive train. The results show that the model training speed is faster than any conventional popular indirect measurement and demonstrates good consistency when compared with the field test data.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 104 PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE Edited by: J. Pombo Paper 90 Online Measuring of the Wheel and Track Interactional Force Indirectly based on an Extreme Learning Machine Approach J.F. Guo, W.D. Wang and J.Z. Liu Infrastructure Inspection Center, China Academy of Railway Sciences, Beijing, China doi:10.4203/ccp.104.90 purchase the full-text of this paper Full Bibliographic Reference for this paper J.F. Guo, W.D. Wang, J.Z. Liu, "Online Measuring of the Wheel and Track Interactional Force Indirectly based on an Extreme Learning Machine Approach", in J. Pombo, (Editor), "Proceedings of the Second International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Stirlingshire, UK, Paper 90, 2014. doi:10.4203/ccp.104.90 Keywords: wheel, track, interactional force, vehicle acceleration, track geometry, extreme learning machine, back propagation. Summary Wheel and track interactional forces can be used to detect track status and guide track maintenance. This paper proposes a new indirect online method to detect wheel and track interactional forces based on an extreme learning machine (ELM) approach using acceleration and track geometry data. This method is different from the traditional methods. The ELM algorithm provides a good generalization in a few minutes with a very fast training speed, so it may be used online. The prediction model for wheel and track interactional forces has been validated by the use of field test data from the high speed comprehensive train. The results show that the model training speed is faster than any conventional popular indirect measurement and demonstrates good consistency when compared with the field test data. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £65 +P&P)
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