[2409.11446] Volvo Discovery Challenge at ECML-PKDD 2024
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Computer Science > Machine Learning

arXiv:2409.11446 (cs)
[Submitted on 17 Sep 2024]

Title:Volvo Discovery Challenge at ECML-PKDD 2024

Authors:Mahmoud Rahat, Peyman Sheikholharam Mashhadi, Sławomir Nowaczyk, Shamik Choudhury, Leo Petrin, Thorsteinn Rognvaldsson, Andreas Voskou, Carlo Metta, Claudio Savelli
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Abstract:This paper presents an overview of the Volvo Discovery Challenge, held during the ECML-PKDD 2024 conference. The challenge's goal was to predict the failure risk of an anonymized component in Volvo trucks using a newly published dataset. The test data included observations from two generations (gen1 and gen2) of the component, while the training data was provided only for gen1. The challenge attracted 52 data scientists from around the world who submitted a total of 791 entries. We provide a brief description of the problem definition, challenge setup, and statistics about the submissions. In the section on winning methodologies, the first, second, and third-place winners of the competition briefly describe their proposed methods and provide GitHub links to their implemented code. The shared code can be interesting as an advanced methodology for researchers in the predictive maintenance domain. The competition was hosted on the Codabench platform.
Comments: ECML/PKDD 2024, Discovery Challenge
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2409.11446 [cs.LG]
  (or arXiv:2409.11446v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2409.11446

Submission history

From: Peyman Mashhadi [view email]
[v1] Tue, 17 Sep 2024 10:05:24 UTC (1,230 KB)
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