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.
Bibliographical noteFunding Information:
This research was supported in part by National Science Foundation (NSF) grants CMMI-1362876 and DMS-1830344, through Georgia Institute of Technology.
© 2020 American Society for Quality.
- adaptive sampling
- change detection
- data fusion
- limited resources
- order thresholding
- partial observations
ASJC Scopus subject areas
- Safety, Risk, Reliability and Quality
- Strategy and Management
- Management Science and Operations Research
- Industrial and Manufacturing Engineering