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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.
Acknowledments
The authors thank the Editor and two anonymous referees for their thoughtful and constructive comments that greatly improved the quality and presentation of this article.
Additional information
Funding
Notes on contributors
Mohammad Nabhan
Dr. Nabhan is an Assistant Professor in the Systems Engineering Department at King Fahd University of Petroleum and Minerals, Dhahran, Kingdom of Saudi Arabia. His email is [email protected]
Yajun Mei
Dr. Mei is an Associate Professor in the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology. He is the corresponding author. His email is [email protected]
Jianjun Shi
Dr. Shi is the Carolyn J. Stewart Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology. His email is [email protected]