Volvo Discovery Challenge at ECML-PKDD 2024
This is an incremental challenge for predictive maintenance researchers, focusing on a domain-specific problem with truck components.
The paper describes the Volvo Discovery Challenge at ECML-PKDD 2024, which tackled predicting failure risk of an anonymized component in Volvo trucks using a new dataset, resulting in 52 participants submitting 791 entries.
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.