Abstract

Effective process monitoring of high-dimensional data streams with embedded spatial structures has been an arising challenge for environments with limited resources. Utilizing the spatial structure is key to improve monitoring performance. This article proposes a correlation-based dynamic sampling technique for change detection. Our method borrows the idea of Upper Confidence Bound algorithm and uses the correlation structure not only to calculate a global statistic, but also to infer unobserved sensors from partial observations. Simulation studies and two case studies on solar flare detection and carbon nanotubes (CNTs) buckypaper process monitoring are used to validate the effectiveness of our method.

摘要

对于资源有限的环境，对具有嵌入式空间结构的高维数据流进行有效的过程监控一直是一个新的挑战。利用空间结构是提高监测性能的关键。本文提出了一种用于变化检测的基于相关性的动态采样技术。我们的方法借用了上限置信度算法的思想，并使用相关结构不仅可以计算全局统计量，还可以从局部观察中推断出未观察到的传感器。模拟研究和两个关于太阳耀斑检测和碳纳米管 (CNT) 巴基纸过程监测的案例研究用于验证我们方法的有效性。