SCANIA Component X Dataset: A Real-World Multivariate Time Series
  Dataset for Predictive Maintenance
Zahra Kharazian Tony Lindgren Sindri Magnússon Olof Steinert Oskar Andersson Reyna
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.