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

2024-01-26

arXiv.org

AI Summary

Predictive maintenance research has long suffered from a scarcity of authentic, multivariate, and temporally complete industrial benchmark datasets. To address this gap, we introduce SCANIA-ComponentX: the first large-scale, fully temporal, multimodal (histogram + counter), component-level anonymized industrial dataset for predictive maintenance, derived from real-world operational, maintenance, and technical specification data of a single engine component (Component X) across Scania’s commercial truck fleet. Leveraging multi-source temporal alignment, statistical feature engineering, and standardized metadata modeling, the dataset enables four core analytical tasks—classification, regression, survival analysis, and anomaly detection. SCANIA-ComponentX fills a critical void in high-fidelity real-world benchmarks, substantially enhancing reproducibility and serving as a widely adopted resource for model evaluation and algorithm development in predictive maintenance research.

Technology Category

• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Multi-modal Vision

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Predicting failures and maintenance time in predictive maintenance is challenging due to the scarcity of comprehensive real-world datasets, and among those available, few are of time series format. This paper introduces a real-world, multivariate time series dataset collected exclusively from a single anonymized engine component (Component X) across a fleet of SCANIA trucks. The dataset includes operational data, repair records, and specifications related to Component X, while maintaining confidentiality through anonymization. It is well-suited for a range of machine learning applications, including classification, regression, survival analysis, and anomaly detection, particularly in predictive maintenance scenarios. The dataset's large population size, diverse features (in the form of histograms and numerical counters), and temporal information make it a unique resource 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.

Problem

• Addresses scarcity of real-world datasets for predictive maintenance.
• Introduces a multivariate time series dataset from SCANIA trucks.
• Aims to establish a benchmark for predictive maintenance research.

Innovation

• Real-world multivariate time series dataset
• Includes operational data and repair records
• Supports machine learning for predictive maintenance