SensorSCAN: Self-Supervised Learning and Deep Clustering for Fault Diagnosis in Chemical Processes. (arXiv:2208.08879v1 [cs.LG])

Modern industrial facilities generate large volumes of raw sensor data during
production process. This data is used to monitor and control the processes and
can be analyzed to detect and predict process abnormalities. Typically, the
data has to be annotated by experts to be further used in predictive modeling.
Most of today's research is focusing on either unsupervised anomaly detection
algorithms or supervised methods, that require manually annotated data. The
studies are often done using process simulator generated data for …

arxiv clustering diagnosis learning lg processes self-supervised learning supervised learning