Correlation-based dynamic sampling for online high dimensional process monitoring

Mohammad Nabhan, Yajun Mei*, Jianjun Shi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


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.

Original languageEnglish
Pages (from-to)289-308
Number of pages20
JournalJournal of Quality Technology
Issue number3
StatePublished - 2021

Bibliographical note

Funding Information:
This research was supported in part by National Science Foundation (NSF) grants CMMI-1362876 and DMS-1830344, through Georgia Institute of Technology.

Publisher Copyright:
© 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


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