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）公司商用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，可反映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保密性。其适用于多种机器学习任务，包括分类、回归、生存分析与异常检测，尤其适配预测性维护场景。该数据集样本规模庞大、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特色的真实世界数据集。发布本数据集的目的在于，为广大研究者提供使用知名国际企业真实业务数据的契机，并为预测性维护领域引入标准化基准数据集，推动可复现研究的发展。

搜集汇总

背景与挑战

背景概述

该数据集是来自瑞典SCANIA公司卡车车队匿名化引擎组件的真实世界多变量时间序列数据，包含操作数据、维修记录和卡车规格等信息，适用于分类、回归、生存分析等预测性维护机器学习任务。其特点是数据量大、特征多样且包含时间维度，旨在为预测性维护研究提供标准基准。