SCANIA Component X Dataset: A Real-World Multivariate Time Series Dataset for Predictive Maintenance

This data is 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 a well-known international company and introduce a standard benchmark to the predictive maintenance field, fostering reproducible research.

本数据集为真实世界多变量时间序列数据集，采集自瑞典斯堪尼亚（SCANIA）公司卡车车队的匿名化发动机部件（命名为部件X）。该数据集包含多类变量，可记录卡车详细运行数据、维修记录与规格参数，并通过匿名化处理保障数据机密性。其适配多种机器学习任务，包括分类、回归、生存分析与异常检测，尤其适用于预测性维护场景。该数据集样本量庞大、特征类型丰富（涵盖直方图格式与数值计数型特征），且包含时序信息，使其成为该领域内极具独特性的真实世界数据集。本数据集发布的初衷，是为广大研究者提供基于知名国际企业真实业务数据开展研究的契机，并为预测性维护领域引入标准化基准数据集，助力可复现研究的发展。