SCANIA Component X Dataset: A Real-World Multivariate Time Series Dataset for Predictive Maintenance (2401.15199v2)
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.
- Turbofan Engine Degradation Simulation Data Set. NASA Prognostics Data Repository, NASA Ames Research Center, Moffett Field, CA (2008). Https://www.nasa.gov/content/diagnostics-prognostics.
- SCANIA component dataset X for predictive maintenance. The 22nd International Symposium on Intelligent Data Analysis (IDA 2024) Industrial Challenge Repository, https://ida2024.org/industrial-challenge/ (2024).
- Low dimensional synthetic data generation for improving data driven prognostic models. In 2022 IEEE International Conference on Prognostics and Health Management (ICPHM), 173–182 (IEEE, 2022).
- APS Failure at Scania Trucks. UCI Machine Learning Repository, {DOI}:https://doi.org/10.24432/C51S51 (2017).
- An energy-efficient and trustworthy unsupervised anomaly detection framework (eatu) for iiot. \JournalTitleACM Transactions on Sensor Networks 18, 1–18 (2022).
- Automated maintenance data classification using recurrent neural network: Enhancement by spotted hyena-based whale optimization. \JournalTitleMathematics 8, 2008 (2020).
- Air pressure system failure prediction and classification in scania trucks using machine learning. In 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS), 220–227 (IEEE, 2022).
- Truck aps failure detection using machine learning. In 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), 307–310 (IEEE, 2020).
- A methodology for prognostics under the conditions of limited failure data availability. \JournalTitleIEEE Access 7, 183996–184007 (2019).
- Quantum mechanics-based missing value estimation framework for industrial data. \JournalTitleExpert Systems with Applications 236, 121385 (2024).
- Deep neural network heuristic hierarchization for cooperative intelligent transportation fleet management. \JournalTitleIEEE Transactions on Intelligent Transportation Systems 23, 16752–16762 (2022).
- Robust contrastive learning and multi-shot voting for high-dimensional multivariate data-driven prognostics. In 2023 IEEE International Conference on Prognostics and Health Management (ICPHM), 53–60 (IEEE, 2023).
- Predictive maintenance of air pressure system using boosting trees: A machine learning approach. In ORSI (2018).
- An empirical comparison of missing value imputation techniques on aps failure prediction. \JournalTitleInternational Journal of Information Technology and Computer Science 2, 21–29 (2019).
- A novel linear classifier for class imbalance data arising in failure-prone air pressure systems. \JournalTitleIEEE Access 9, 4211–4222 (2020).
- Minimizing the repair cost of the air pressure system of scania trucks using a deep learning algorithm. \JournalTitleTechRxiv (2023).
- Beikmohammadi, A. et al. A cost-sensitive transformer model for prognostics under highly imbalanced industrial data. \JournalTitlearXiv preprint arXiv:2401.08115 (2024).
- Survival analysis and predictive maintenance models for non-sensored assets in facilities management. In 2021 IEEE international conference on big data (Big Data), 4026–4034 (IEEE, 2021).
- Bridging the gap: A comparative analysis of regressive remaining useful life prediction and survival analysis methods for predictive maintenance. In PHM Society Asia-Pacific Conference, vol. 4 (2023).
- Roadmap for a successful implementation of a predictive maintenance strategy. \JournalTitleSmart and Sustainable Supply Chain and Logistics–Trends, Challenges, Methods and Best Practices: Volume 1 423–439 (2020).
- Modeling turbocharger failures using markov process for predictive maintenance. In 30th European Safety and Reliability Conference (ESREL2020) & 15th Probabilistic Safety Assessment and Management Conference (PSAM15), Venice, Italy, 1-5 November, 2020 (European Safety and Reliability Association, 2020).
- Predictive maintenance in building facilities: A machine learning-based approach. \JournalTitleSensors 21, 1044 (2021).
- Rahat, M. et al. Domain adaptation in predicting turbocharger failures using vehicle’s sensor measurements. In PHM Society European Conference, vol. 7, 432–439 (2022).
- Licence. Https://creativecommons.org/licenses/by/4.0/.
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