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
Parents
319
Total projection to latent structures for process monitoring
Donghua Zhou, Gang Li, S. Joe Qin · AIChE Journal · 2009 · 469 citations
Monitoring independent components for fault detection
Manabu Kano, Shouhei Tanaka, Shinji Hasebe et al. · AIChE Journal · 2003 · 410 citations
1–5 of 319
782
12
h-index
12
h-index
King Abdullah University of Science and Technology, Saudi Arabia
9 publications · 388 citations
9
h-index
8
h-index
7
h-index
1–5 of 782
300
Texas A&M University at Qatar
Doha, Qatar
13 h-index
43 publications
2.5K citations
East China University of Science and Technology
Shanghai, China
13 h-index
44 publications
2.1K citations
Tsinghua University
Beijing, China
12 h-index
33 publications
4.6K citations

Zhejiang University of Technology
Hangzhou, China
11 h-index
29 publications
802 citations
Pohang University of Science and Technology
Pohang, South Korea
9 h-index
17 publications
1.4K citations

Northeastern University
Shenyang, China
9 h-index
21 publications
799 citations

McMaster University
Hamilton, Canada
9 h-index
15 publications
1.8K citations

Shenyang University of Chemical Technology
Shenyang, China
8 h-index
20 publications
440 citations
1–8 of 300
27 publications · 1.4K citations
22
h-index
18 publications · 1.7K citations
17
h-index
17 publications · 1.2K citations
16
h-index
13 publications · 2.7K citations
13
h-index
11 publications · 519 citations
10
h-index
1–5