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.

翻译：统计过程监控（SPM）方法是质量管理中用于检查工业过程稳定性的重要工具，即动态地将过程状态分类为受控状态（IC，处于正常运行条件下）或失控状态（OC，其他情况）。传统的SPM方法基于无监督方法，这类方法之所以流行，是因为在大多数工业应用中，过程的真实OC状态并不明确已知。这阻碍了监督方法的发展，尽管监督方法可以利用包含真实过程状态标签的过程数据，但在处理类别不平衡（因为在高品质过程中OC状态较为罕见）以及动态识别未见类别（例如可能的OC状态数量）方面仍需改进。本文提出了一种新颖的基于数据流的主动学习策略用于SPM，该策略通过增强部分隐马尔可夫模型来处理数据流。其最终目标是在有限预算约束下优化标注资源，并动态更新可能的OC状态。通过仿真以及在汽车行业电阻点焊过程的SPM案例研究（本研究即受此启发），评估了所提方法在分类过程真实状态方面的性能。