Abstract:Machine learning (ML) holds great potential to advance anomaly detection (AD) in chemical processes. However, the development of ML-based methods is hindered by the lack of openly available experimental data. To address this gap, we have set up a laboratory-scale batch distillation plant and operated it to generate an extensive experimental database, covering fault-free experiments and experiments in which anomalies were intentionally induced, for training advanced ML-based AD methods. In total, 119 experiments were conducted across a wide range of operating conditions and mixtures. Most experiments containing anomalies were paired with a corresponding fault-free one. The database that we provide here includes time-series data from numerous sensors and actuators, along with estimates of measurement uncertainty. In addition, unconventional data sources -- such as concentration profiles obtained via online benchtop NMR spectroscopy and video and audio recordings -- are provided. Extensive metadata and expert annotations of all experiments are included. The anomaly annotations are based on an ontology developed in this work. The data are organized in a structured database and made freely available via this http URL. This new database paves the way for the development of advanced ML-based AD methods. As it includes information on the causes of anomalies, it further enables the development of interpretable and explainable ML approaches, as well as methods for anomaly mitigation.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2510.18075 [cs.LG] |
| (or arXiv:2510.18075v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2510.18075 arXiv-issued DOI via DataCite |
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| Journal reference: | Sci. Data 13 (2026) 513 |
| Related DOI: | https://doi.org/10.1038/s41597-026-07124-3
DOI(s) linking to related resources |
From: Justus Arweiler [view email]
[v1]
Mon, 20 Oct 2025 20:13:31 UTC (18,165 KB)
[v2]
Fri, 10 Apr 2026 10:42:28 UTC (11,887 KB)
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