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

Tony Lindgren - Stockholm University, Department of Computer and Systems Sciences, DSV

Olof Steinert - Scania CV AB, Strategic Product Planning and Advanced Analytics

Oskar Andersson Reyna - Scania CV AB, Connected Intelligence

Zahra Kharazian - Stockholm University, Department of Computer and Systems Sciences, DSV

Sindri Magnússon - Stockholm University, Department of Computer and Systems Sciences, DSV

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.

English