Abstract
Fault detection of nonlinear processes using Kernel Principal Component Analysis(KPCA) method has recently prompt a lot of interest due to its industrial practical importance. However, this method cannot be applied for data sets with a large amount of samples. To overcome this deficiency, this paper proposes a reduced KPCA method based on K-means clustering. This method aims to find a reduced data set among the training data in the input space and uses this reduced data set to built the reduced KPCA model in the feature space. The relevance of the proposed method is illustrated on an air quality monitoring network. The simulation results demonstrate the effectiveness of the new method when compared to the classical KPCA technique.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 3159-3164 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781538666500 |
| DOIs | |
| Publication status | Published - 16 Jan 2019 |
| Event | 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 - Miyazaki, Japan Duration: 7 Oct 2018 → 10 Oct 2018 |
Publication series
| Name | Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 |
|---|
Conference
| Conference | 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 |
|---|---|
| Country | Japan |
| City | Miyazaki |
| Period | 7/10/18 → 10/10/18 |
Fingerprint
Keywords
- AIRLOR
- fault detection
- K-means
- KPCA
- Nonlinear process
- PCA
ASJC Scopus subject areas
- Information Systems
- Information Systems and Management
- Health Informatics
- Artificial Intelligence
- Computer Networks and Communications
- Human-Computer Interaction
Cite this
Reduced Kernel Principal Component Analysis for Fault Detection and Its Application to an Air Quality Monitoring Network. / Fezai, Radhia; Mansouri, Majdi; Taouali, Okba; Harkat, Mohamed-Faouzi; Nounou, Hazem.
Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc., 2019. p. 3159-3164 8616532 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Reduced Kernel Principal Component Analysis for Fault Detection and Its Application to an Air Quality Monitoring Network
AU - Fezai, Radhia
AU - Mansouri, Majdi
AU - Taouali, Okba
AU - Harkat, Mohamed-Faouzi
AU - Nounou, Hazem
PY - 2019/1/16
Y1 - 2019/1/16
N2 - Fault detection of nonlinear processes using Kernel Principal Component Analysis(KPCA) method has recently prompt a lot of interest due to its industrial practical importance. However, this method cannot be applied for data sets with a large amount of samples. To overcome this deficiency, this paper proposes a reduced KPCA method based on K-means clustering. This method aims to find a reduced data set among the training data in the input space and uses this reduced data set to built the reduced KPCA model in the feature space. The relevance of the proposed method is illustrated on an air quality monitoring network. The simulation results demonstrate the effectiveness of the new method when compared to the classical KPCA technique.
AB - Fault detection of nonlinear processes using Kernel Principal Component Analysis(KPCA) method has recently prompt a lot of interest due to its industrial practical importance. However, this method cannot be applied for data sets with a large amount of samples. To overcome this deficiency, this paper proposes a reduced KPCA method based on K-means clustering. This method aims to find a reduced data set among the training data in the input space and uses this reduced data set to built the reduced KPCA model in the feature space. The relevance of the proposed method is illustrated on an air quality monitoring network. The simulation results demonstrate the effectiveness of the new method when compared to the classical KPCA technique.
KW - AIRLOR
KW - fault detection
KW - K-means
KW - KPCA
KW - Nonlinear process
KW - PCA
UR - http://www.scopus.com/inward/record.url?scp=85062222312&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062222312&partnerID=8YFLogxK
U2 - 10.1109/SMC.2018.00535
DO - 10.1109/SMC.2018.00535
M3 - Conference contribution
T3 - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
SP - 3159
EP - 3164
BT - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -