Summary of Stream-based Active Learning For Process Monitoring, by Christian Capezza et al.
Stream-Based Active Learning for Process Monitoring
by Christian Capezza, Antonio Lepore, Kamran Paynabar
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel stream-based active learning strategy for statistical process monitoring (SPM) to dynamically classify industrial processes as in control or out of control. Traditional unsupervised SPM methods are widely used due to the lack of labeled data, but this approach has limitations when dealing with class imbalance and unseen classes. The proposed method enhances partially hidden Markov models to handle data streams, aiming to optimize labeling resources while dynamically updating possible out-of-control states. A simulation and a case study on resistance spot welding process in the automotive industry demonstrate the effectiveness of the proposed method in classifying the true state of the process. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is about improving how we monitor industrial processes to make sure they’re running smoothly or not. We currently use methods that don’t need labeled data, but these have limitations when dealing with unusual situations. The goal is to create a new approach that uses limited labeled data to optimize the process monitoring and dynamically recognize unexpected problems. This paper presents a new method that handles this challenge and tests it on a real-world example from the automotive industry. |
Keywords
* Artificial intelligence * Active learning * Unsupervised
