Multivariate Statistical Fault Detection

Multivariate Statistical Fault Detection is a fault detection methodology that uses multivariate statistical models to monitor multiple process variables simultaneously and identify abnormal conditions by analyzing their collective deviations from normal operating behavior.

319

Publications

19.3K

Citations

782

Authors

300

Institutions

Publications per year

2017–2026

119

Authors

782

  1. MN
    Mohamed Nounou

    Texas A&M University at Qatar, Qatar

    12 publications · 662 citations

    12

    h-index

  2. HN
    Hazem Nounou

    Texas A&M University at Qatar, Qatar

    13 publications · 693 citations

    12

    h-index

  3. FH
    Fouzi Harrou

    King Abdullah University of Science and Technology, Saudi Arabia

    9 publications · 388 citations

    9

    h-index

  4. YL
    Yuan Li

    Shenyang University of Chemical Technology, China

    8 publications · 190 citations

    8

    h-index

  5. XY
    Xuefeng Yan

    East China University of Science and Technology, China

    7 publications · 396 citations

    7

    h-index

1–5 of 782

Venues

  1. Industrial & Engineering Chemistry Research

    27 publications · 1.4K citations

    22

    h-index

  2. Chemometrics and Intelligent Laboratory Systems

    18 publications · 1.7K citations

    17

    h-index

  3. Journal of Process Control

    17 publications · 1.2K citations

    16

    h-index

  4. AIChE Journal

    13 publications · 2.7K citations

    13

    h-index

  5. Journal of Chemometrics

    11 publications · 519 citations

    10

    h-index

1–5