Abstract
Today industrial facilities are equipped with lots of sensors throughout all the production line for monitoring means. Gathered data can be used to detect and predict failures; however, manual labeling of large amounts of data for supervised learning is complicated. This paper introduces an innovative approach to unsupervised fault detection and diagnosis tailored for monitoring industrial chemical processes. We showcase the efficacy of our model using two publicly accessible datasets from the Tennessee Eastman Process, each containing various faults. Furthermore, we illustrate that by fine-tuning the model on a limited amount of labeled data, it achieves performance close to that of a state-of-the-art model trained on the entire dataset.
Keywords
- Data Mining: DM: Anomaly/outlier detection
- Machine Learning: ML: Clustering
- Machine Learning: ML: Self-supervised Learning
- Machine Learning: ML: Time series and data streams
- Machine Learning: ML: Unsupervised learning
