Stream-Based Active Learning for Process Monitoring

Statistical process monitoring (SPM) methods are essential tools in quality management to assess 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. Although traditional SPM methods are based on unsupervised approaches, supervised methods leverage process data with labels revealing the true process state. However, labeling procedures are often expensive, making it impractical to annotate all data points. To address this challenge, we propose a novel stream-based active learning strategy for SPM that selects the most informative data points for labeling under a limited budget. While traditional active learning methods assume independently distributed data, we explicitly account for temporal dependencies in data streams, leveraging partially hidden Markov models to integrate labeled and unlabeled observations. The proposed method balances exploration, to detect unseen process states, and exploitation, to refine classification accuracy within known states, and extends pool-based active learning to real-time settings by providing labeling decisions for each incoming data point. The proposed method’s performance in classifying the process state is assessed through a simulation and a case study on monitoring a resistance spot welding process in the automotive industry, which motivated this research.