Event-Triggered Bayesian Control Chart
提出一种利用智能传感器实时数据的事件触发贝叶斯控制图，联合决策采样时机和停止生产时机，实现快速故障检测并降低采样成本，适用于工业过程监控。
Control charts are practical tools for fault detection and recovery. However, traditional control charts rely on random samples collected from a production process at fixed time intervals, causing late detection if sampling intervals are too long or excessive sampling if the intervals are too short. In “Event-Triggered Bayesian Control Chart,” Abbou and Makis develop a novel control chart leveraging real-time data from smart sensors to jointly decide when to collect samples and when to stop the production process, leading to quick fault detection and recovery using few samples. Applying optimal stopping theory and dynamic programming analysis, the authors establish the average-cost optimality of their control chart and propose an efficient procedure for computing the optimal sampling and stopping thresholds. Through an empirical study, the control chart is shown to achieve substantial cost savings compared to benchmarks. Furthermore, thanks to its event-triggering mechanism, the proposed control chart requires little data communication from sensors, which is crucial from an energy-efficiency perspective.