This paper presents a description of a real-world, multivariate time series
dataset collected from an anonymized engine component (called Component X) of a
fleet of trucks from SCANIA, Sweden. This dataset includes diverse variables
capturing detailed operational data, repair records, and specifications of
trucks while maintaining confidentiality by anonymization. It is well-suited
for a range of machine learning applications, such as classification,
regression, survival analysis, and anomaly detection, particularly when applied
to predictive maintenance scenarios. The large population size and variety of
features in the format of histograms and numerical counters, along with the
inclusion of temporal information, make this real-world dataset unique in the
field. The objective of releasing this dataset is to give a broad range of
researchers the possibility of working with real-world data from an
internationally well-known company and introduce a standard benchmark to the
predictive maintenance field, fostering reproducible research.

本文介绍了一篇关于来自 SCANIA，瑞典一批卡车的一个匿名化引擎组件（称为 X 组件）的实际多变量时间序列数据集的描述。这个数据集包括捕获详细操作数据、维修记录和卡车规格的多种变量，通过匿名化保持机密性。该数据集非常适合于各种机器学习应用，如分类、回归、生存分析和异常检测，尤其适用于预测维护场景。大规模的人口数量以及采用直方图和数字计数器格式的各种特征，再加上包含时间信息，使这个实际数据集在该领域中显得独特。发布这个数据集的目的是为各种研究人员提供从国际知名公司获取实际数据并引入到预测维护领域的标准基准，促进可再现性研究。