CoChat
Sign Up
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
Open this paper in CoChat — free account

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.

Overview

Integrity signals

Registry

Crossref · 10.1109/tits.2017.2720721

Cites

36 of 36 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Flagged first, then by citations

Show all 37 references

1 of the 37 references couldn’t be matched to a known paper.

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

Cited by

Sorted by citations

Items 101–200 of 258

Related

See all

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review