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Online adaptive learning for out-of-round railway wheels detection

Published: 07 June 2023 Publication History

Abstract

Wheel out-of-roundness is one of the most important causes of train accidents. With the Internet of Things rapidly expanding, many opportunities arise for the implementation of real-time condition-based maintenance techniques. The purpose of this work was building an online adaptive learning algorithm for wheel polygonization detection with different wavelengths. Using efficient pre-processing techniques to deal with the open-ended and non-stationary nature of an accelerometer data stream, this method automatically detected train passages, segmented groups of axles and ranked damage severity, accounting for variability in train speed and the unevenness profile of rail. Experimental results show the proposed model successfully ranks the polygonization of individual wheels, establishing a useful priority sequence for replacement interventions.

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Cited By

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  • (2024)Time series data mining for railway wheel and track monitoring: a surveyNeural Computing and Applications10.1007/s00521-024-10138-w36:27(16707-16725)Online publication date: 24-Jul-2024
  • (2023)Adaptive time series representation for out-of-round railway wheels fault diagnosis in wayside monitoringEngineering Failure Analysis10.1016/j.engfailanal.2023.107433152(107433)Online publication date: Oct-2023

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cover image ACM Conferences
SAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing
March 2023
1932 pages
ISBN:9781450395175
DOI:10.1145/3555776
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

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Publication History

Published: 07 June 2023

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Author Tags

  1. data streams
  2. machine learning
  3. fault detection
  4. wheel out-of-roundness
  5. polygonization

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  • FERROVIA 4.0
  • FCT (Portuguese Foundation for Science and Technology)

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SAC '23
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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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SAC '25
The 40th ACM/SIGAPP Symposium on Applied Computing
March 31 - April 4, 2025
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Cited By

View all
  • (2024)Time series data mining for railway wheel and track monitoring: a surveyNeural Computing and Applications10.1007/s00521-024-10138-w36:27(16707-16725)Online publication date: 24-Jul-2024
  • (2023)Adaptive time series representation for out-of-round railway wheels fault diagnosis in wayside monitoringEngineering Failure Analysis10.1016/j.engfailanal.2023.107433152(107433)Online publication date: Oct-2023

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