NoBOOM: Chemical Process Datasets for Industrial Anomaly Detection
Dennis Wagner, Fabian Hartung, Justus Arweiler, Aparna Muraleedharan, Indra Jungjohann, Arjun Nair, Steffen Reithermann, Ralf Schulz, Michael Bortz, Daniel Neider, Heike Leitte, Joachim Pfeffinger, Stephan Mandt, Sophie Burkhardt, Torsten Katz, Fabian Jirasek, Jakob Burger, Hans Hasse, Marius Kloft
Monitoring chemical processes is necessary to prevent catastrophic failures, optimize costs and profits, and ensure the safety of employees and the environment. A key component of modern monitoring systems is the automated detection of anomalies in sensor data over time, called time series, enabling partial automation of plant operation and adding additional layers of supervision to crucial components. The development of anomaly detection  methods in this domain is challenging, since real chemical process data is usually proprietary, and simulated data is generally not a sufficient replacement. In this paper, we present NoBOOM, the first collection of datasets for anomaly detection in real-life chemical process data, including labeled data from a running process at a leading industry partner, and several chemical processes run in a laboratory‑scale plant and a pilot‑scale plant. While we are not able to share every detail about the industrial process, for the laboratory‑ and pilot‑scale plants, we provide comprehensive information on plant configuration, processes run, operation, and, in particular, anomaly events, enabling a differentiated analysis of anomaly detection methods. To demonstrate the complexity of the benchmark, we analyze the data with regard to common issues of time-series anomaly detection (TSAD) benchmarks, including triviality and biases.