Author

Aparna Muraleedharan

h-index 7 View on Semantic Scholar

We have 2 of 30 papers

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Open access Oct 2026

Time series data with and without anomalies from a continuous distillation plant

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

2025

NoBOOM: Chemical Process Datasets for Industrial Anomaly Detection

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