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.

本数据集为瑞典斯堪尼亚（SCANIA）旗下卡车车队的匿名化发动机部件（组件X，Component X）所采集的真实多变量时间序列数据集。该数据集包含多元变量，可完整捕捉卡车的详细运行数据、维修记录与技术规格，并通过匿名化处理保障数据机密性。其可适配多种机器学习应用场景，涵盖分类、回归、生存分析（survival analysis）与异常检测等任务，尤其适用于预测性维护（predictive maintenance）相关场景。该数据集样本规模庞大、特征类型丰富，特征涵盖直方图格式与数值计数型数据，且纳入时序维度信息，使其成为该领域中独具特色的真实世界数据集。本数据集的发布旨在为广大研究者提供接触知名国际企业真实工业数据的契机，并为预测性维护领域引入标准化基准测试集，以推动可复现的学术研究工作。

搜集汇总

背景与挑战

背景概述

该数据集是来自SCANIA卡车的匿名化发动机组件多元时间序列数据，包含操作数据、维修记录和车辆规格，专为预测性维护的机器学习应用设计。数据集具有真实世界数据的多样性和时序特征，适合分类、回归、生存分析等多种分析任务。