2 research outputs found
SensorSCAN: Self-Supervised Learning and Deep Clustering for Fault Diagnosis in Chemical Processes
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
Refining the ONCE Benchmark With Hyperparameter Tuning
In response to the growing demand for 3D object detection in applications such as autonomous driving, robotics, and augmented reality, this work focuses on the evaluation of semi-supervised learning approaches for point cloud data. The point cloud representation provides reliable and consistent observations regardless of lighting conditions, thanks to advances in LiDAR sensors. Data annotation is of paramount importance in the context of LiDAR applications, and automating 3D data annotation with semi-supervised methods is a pivotal challenge that promises to reduce the associated workload and facilitate the emergence of cost-effective LiDAR solutions. Nevertheless, the task of semi-supervised learning in the context of unordered point cloud data remains formidable due to the inherent sparsity and incomplete shapes that hinder the generation of accurate pseudo-labels. In this study, we consider these challenges by posing the question: “To what extent does unlabelled data contribute to the enhancement of model performance?” We show that improvements from previous semi-supervised methods may not be as profound as previously thought. Our results suggest that simple grid search hyperparameter tuning applied to a supervised model can lead to state-of-the-art performance on the ONCE dataset, while the contribution of unlabelled data appears to be comparatively less exceptional