Abstract
The paper presents a measurement setup capable of collecting wheel/rail contact noise and vibration signals from a passenger train. A data analysis method based on machine learning is developed for detecting events from the acquired data and classifying them according to relevant railway track components and noise phenomena. A classification rate higher than 84 % is achieved.
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Lee, J.S., Choi, S., Kim, S.S., Park, C., Kim, Y.G.: A mixed filtering approach for track condition monitoring using accelerometers on the axle box and bogie. IEEE Transactions on Instrumentation and Measurement 61, 749–758 (2012)
Mori, H., Tsunashima, H., Kojima, T., Matsumoto, A., Mizuma, T.: Condition monitoring of railway track using in-service vehicle. Journal of Mechanical Systems for Transportation and Logistics 3, 154–165 (2010)
Lozano-Angulo, J.A.: Detection and one class classification of transient events in train track noise. Master’s Thesis, Technical University of Denmark, Denmark (2012)
Masri, P.: Computed modelling of sound for transformation and synthesis of musical signal. Ph.D. dissertation, University of Bristol, UK (1996)
Fawcett, T.: An introduction to ROC analysis. Pattern Recognition Letters 27, 861–874 (2006)
Ye, Q., Zhao, C.X., Zhang, H.F., Chen, X.B.: Recursive ‘concave–convex’ Fisher Linear Discriminant with applications to face, handwritten digit and terrain recognition. Pattern Recognition 45(1), 54–65 (2012)
Mediante, E.C.: Sound recognition techniques: application to city noise. Bachelor thesis, Technical University of Denmark, Denmark (2012)
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© 2015 Springer-Verlag Berlin Heidelberg
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Jensen, T., Chauhan, S., Haddad, K., Song, W., Junge, S. (2015). Monitoring Rail Condition Based on Sound and Vibration Sensors Installed on an Operational Train. In: Nielsen, J., et al. Noise and Vibration Mitigation for Rail Transportation Systems. Notes on Numerical Fluid Mechanics and Multidisciplinary Design, vol 126. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44832-8_27
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DOI: https://doi.org/10.1007/978-3-662-44832-8_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-44831-1
Online ISBN: 978-3-662-44832-8
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