Multivariate Statistical Process Monitoring

Multivariate Statistical Process Monitoring is a statistical methodology within process monitoring that simultaneously analyzes multiple interrelated process variables using multivariate statistical techniques to detect abnormal patterns or trends indicative of process drift or failure in the context of Continued Process Verification.

394

Publications

26.6K

Citations

871

Authors

356

Institutions

Publications per year

2017–2026

116

Authors

871

  1. SJ
    S. Joe Qin

    University of Southern California, United States

    11 publications · 2K citations

    11

    h-index

  2. XY
    Xuefeng Yan

    East China University of Science and Technology, China

    11 publications · 1.1K citations

    11

    h-index

  3. ST
    Seyed Taghi Akhavan Niaki

    Sharif University of Technology, Iran

    9 publications · 178 citations

    9

    h-index

  4. YY
    Yuan Yao

    National Tsing Hua University, Taiwan

    9 publications · 361 citations

    9

    h-index

  5. IL
    In−Beum Lee

    Pohang University of Science and Technology, South Korea

    8 publications · 803 citations

    8

    h-index

1–5 of 871

Venues

  1. Industrial & Engineering Chemistry Research

    31 publications · 2.4K citations

    22

    h-index

  2. Journal of Process Control

    21 publications · 1.5K citations

    21

    h-index

  3. AIChE Journal

    20 publications · 5.5K citations

    18

    h-index

  4. Chemometrics and Intelligent Laboratory Systems

    17 publications · 1.5K citations

    17

    h-index

  5. Journal of Quality Technology

    18 publications · 1.5K citations

    14

    h-index

1–5