  Modern industrial facilities generate large volumes of raw sensor data during
the 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 in order to be used in predictive modeling.
However, manual annotation of large amounts of data can be difficult in
industrial settings.
  In this paper, we propose SensorSCAN, a novel method for unsupervised fault
detection and diagnosis, designed for industrial chemical process monitoring.
We demonstrate our model's performance on two publicly available datasets of
the Tennessee Eastman Process with various faults. The results show that our
method significantly outperforms existing approaches (+0.2-0.3 TPR for a fixed
FPR) and effectively detects most of the process faults without expert
annotation. Moreover, we show that the model fine-tuned on a small fraction of
labeled data nearly reaches the performance of a SOTA model trained on the full
dataset. We also demonstrate that our method is suitable for real-world
applications where the number of faults is not known in advance. The code is
available at https://github.com/AIRI-Institute/sensorscan.
