Reliable detection of process anomalies remains a challenge in industrial chemical plants. The ability of machine learning (ML) to recognize patterns has triggered numerous research efforts to apply ML to anomaly detection (AD). Simulation-based benchmarks, such as the Tennessee Eastman process, are widely used to deve...
Aparna Muraleedharan, A. Ferre, J. Arweiler et al.· Scientific Data· 0 citations
NoBOOM is presented, the first collection of datasets for anomaly detection in real-world chemical process data, including labeled data from a running process at BASF SE, one of the world’s leading chemical companies.
Dennis Wagner, Fabian Hartung, J. Arweiler et al.· Neural Information Processin...· 2 citations
This work augments a large, fully annotated experimental dataset for batch distillation with a corresponding simulation dataset, creating a novel hybrid dataset that provides a unique basis for simulation-to-experiment style transfer, the generation of pseudo-experimental data, and future research on deep AD methods in...
Jennifer Werner, J. Arweiler, Indra Jungjohann et al.· Computers & Chemical Eng...· 0 citations
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