用新数据集预测沃尔沃卡车部件故障风险，吸引全球52位数据科学家参赛。
Volvo Discovery Challenge at ECML-PKDD 2024
- 基于gen1数据训练，预测gen1与gen2部件的故障风险。
- 共提交791次结果，前三名方法在预测准确率上表现领先。
- 代码开源，适合关注预测性维护的研究者参考。
本文介绍了在ECML-PKDD 2024会议期间举办的沃尔沃发现挑战赛。该挑战的目标是利用一个新发布的数据集，预测沃尔沃卡车中一个匿名部件的故障风险。测试数据包含该部件的两代产品（gen1和gen2）的观测记录，而训练数据仅提供gen1。挑战吸引了来自世界各地的52位数据科学家，共提交了791次参赛作品。本文简要描述了问题定义、挑战设置以及提交结果的统计信息。在获奖方法部分，比赛的一、二、三等奖得主分别介绍了其提出的解决方案，并提供了实现代码的GitHub链接。这些共享代码对预测性维护领域的研究人员具有参考价值。竞赛在Codabench平台上举办。
原文摘要 · Abstract (English)
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.
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