# Stream-Based Active Learning for Process Monitoring

arXiv: https://arxiv.org/abs/2411.12563 · HTML: https://mlsanity.com/paper/2411.12563/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 58 references that are in the map.
- First posted on arXiv 19 Nov 2024.

## Builds on (most cited first, 8 of 8)

- [Active learning for data streams: a survey](https://mlsanity.com/paper/2302.08893/) (2023)
- [Streaming Active Learning Strategies for Real-Life Credit Card Fraud Detection: Assessment and Visualization](https://mlsanity.com/paper/1804.07481/) (2018)
- [A Continual Learning Framework for Adaptive Defect Classification and Inspection](https://mlsanity.com/paper/2203.08796/) (2022)
- [Stream-based Active Learning by Exploiting Temporal Properties in Perception with Temporal Predicted Loss](https://mlsanity.com/paper/2309.05517/) (2023)
- [Stream-based Active Learning with Verification Latency in Non-stationary Environments](https://mlsanity.com/paper/2204.06822/) (2022)
- [Ensemble Active Learning by Contextual Bandits for AI Incubation in Manufacturing](https://mlsanity.com/paper/2310.06306/) (2023)
- [Robust Multivariate Functional Control Chart](https://mlsanity.com/paper/2207.07978/) (2022)
- [An Adaptive Multivariate Functional EWMA Control Chart](https://mlsanity.com/paper/2403.03837/) (2024)

## Abstract (arXiv)

Statistical process monitoring (SPM) methods are essential tools in quality management to check the stability of industrial processes, i.e., to dynamically classify the process state as in control (IC), under normal operating conditions, or out of control (OC), otherwise. Traditional SPM methods are based on unsupervised approaches, which are popular because in most industrial applications the true OC states of the process are not explicitly known. This hampered the development of supervised methods that could instead take advantage of process data containing labels on the true process state, although they still need improvement in dealing with class imbalance, as OC states are rare in high-quality processes, and the dynamic recognition of unseen classes, e.g., the number of possible OC states. This article presents a novel stream-based active learning strategy for SPM that enhances partially hidden Markov models to deal with data streams. The ultimate goal is to optimize labeling resources constrained by a limited budget and dynamically update the possible OC states. The proposed method performance in classifying the true state of the process is assessed through a simulation and a case study on the SPM of a resistance spot welding process in the automotive industry, which motivated this research.