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Cited by 258Railway Engineering and DynamicsMachine Fault Diagnosis TechniquesTransportation Systems and Safety

The DOI matches its Crossref record; 36 of the 37 references it cites were found in a registry or an index.

Wheel Defect Detection With Machine Learning

Krummenacher, Gabriel, Ong, Cheng Soon, Koller, Stefan, Kobayashi, Seijin, Buhmann, Joachim M.·2018·IEEE Transactions on Intelligent Transportation Systems
DOI 10.1109/tits.2017.2720721
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Abstract

Wheel defects on railway wagons have been identified as an important source of damage to the railway infrastructure and rolling stock. They also cause noise and vibration emissions that are costly to mitigate. We propose two machine learning methods to automatically detect these wheel defects, based on the wheel vertical force measured by a permanently installed sensor system on the railway network. Our methods automatically learn different types of wheel defects and predict during normal operation if a wheel has a defect or not. The first method is based on novel features for classifying time series data and it is used for classification with a support vector machine. To evaluate the performance of our method we construct multiple data sets for the following defect types: flat spot, shelling, and non-roundness. We outperform classical defect detection methods for flat spots and demonstrate prediction for the other two defect types for the first time. Motivated by the recent success of artificial neural networks for image classification, we train custom artificial neural networks with convolutional layers on 2-D representations of the measurement time series. The neural network approach improves the performance on wheels with flat spots and non-roundness by explicitly modeling the multi sensor structure of the measurement system through multiple instance learning and shift invariant networks.

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Research works printed but not linked: 2

  1. badran, 2011, Wayside train monitoring systems: Networking for greater safety, European Railway Review, 17, 14
  2. 10.1137/1.9781611970104 · DOI · Paper page

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Metadata

Authors
Krummenacher, G., Ong, C. S., Koller, S., Kobayashi, S., & Buhmann, J. M.
Affiliations
  • ETH Zurich (CH)
  • Commonwealth Scientific and Industrial Research Organisation (AU)
ORCID
Venue
IEEE Transactions on Intelligent Transportation Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2018-04
Volume
19
Issue
4
Pages
1176-1187
ISSN
  • 1524-9050 (ISSN-L)
  • 1558-0016
DOI
10.1109/tits.2017.2720721
OpenAlex
W2744245146
Cited by
258
References
37
Citations by year
2026: 15 · 2025: 32 · 2024: 35 · 2023: 38 · 2022: 28 · 2021: 42 · 2020: 35 · 2019: 22 · 2018: 9
FWCI
14.45
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations
IEEE Transactions on Intelligent Transportation Systems (publishedVersion)
Topics
  • Physical Sciences › Engineering › Mechanical Engineering › Railway Engineering and Dynamics
  • Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
  • Physical Sciences › Engineering › Control and Systems Engineering › Transportation Systems and Safety
Keywords
wheel defect detection, freight cars, flat spots, shelling, time series classification, support vector machine, convolutional neural networks, multiple instance learning, railway infrastructure damage

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