Optimized Class Decomposition for Fault Detection

Saso Karakatic; Dusan Fister; Omer Faruk Beyca; Iztok Fister

doi:10.1109/CINTI53070.2021.9668488

Optimized Class Decomposition for Fault Detection

Saso Karakatic^*, Dusan Fister, Omer Faruk Beyca, Iztok Fister

^*Bu çalışma için yazışmadan sorumlu yazar

Endüstri Mühendisliği Bölümü

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

Özet

The paper proposes an innovative approach in solving the fault detection problem of sewerage treatment plant machinery. The proposed approach treats the fault detection data with the class decomposition problem, ensuring that a classification algorithm overlooks no disjunct instances. As the class decomposition technique requires heavy customization to each class of instances in every data set, Grey Wolf Optimizer is used to determine the appropriate clustering method with the appropriate setting for each class of instances. The proposed approach is tested on real-life sensor data from a sewerage treatment plant, and the results show that here proposed approach overshadows several manually proposed class decomposition methods.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	21st IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2021 - Proceedings
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
Sayfalar	15-20
Sayfa sayısı	6
ISBN (Elektronik)	9781665426848
DOI'lar	https://doi.org/10.1109/CINTI53070.2021.9668488
Yayın durumu	Yayınlandı - 2021
Etkinlik	21st IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2021 - Budapest, Hungary Süre: 18 Kas 2021 → 20 Kas 2021

Yayın serisi

Adı	21st IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2021 - Proceedings

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	21st IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2021
Ülke/Bölge	Hungary
Şehir	Budapest
Periyot	18/11/21 → 20/11/21

Bibliyografik not

Publisher Copyright:
© 2021 IEEE.

Finansman

ACKNOWLEDGMENT This research was funded by the Slovenian Research Agency (research cores funding Nos. P2-0057 and P5-0027).

Finansörler	Finansör numarası
Javna Agencija za Raziskovalno Dejavnost RS	P2-0057, P5-0027

Belgeye Erişim

10.1109/CINTI53070.2021.9668488

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Karakatic, S., Fister, D., Beyca, O. F., & Fister, I. (2021). Optimized Class Decomposition for Fault Detection. In 21st IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2021 - Proceedings (pp. 15-20). (21st IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2021 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CINTI53070.2021.9668488

Karakatic, Saso ; Fister, Dusan ; Beyca, Omer Faruk et al. / Optimized Class Decomposition for Fault Detection. 21st IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 15-20 (21st IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2021 - Proceedings).

@inproceedings{960de565763b4b7885388ebfa3f30210,

title = "Optimized Class Decomposition for Fault Detection",

abstract = "The paper proposes an innovative approach in solving the fault detection problem of sewerage treatment plant machinery. The proposed approach treats the fault detection data with the class decomposition problem, ensuring that a classification algorithm overlooks no disjunct instances. As the class decomposition technique requires heavy customization to each class of instances in every data set, Grey Wolf Optimizer is used to determine the appropriate clustering method with the appropriate setting for each class of instances. The proposed approach is tested on real-life sensor data from a sewerage treatment plant, and the results show that here proposed approach overshadows several manually proposed class decomposition methods.",

keywords = "Class Decomposition, Clustering, Fault Detection, Grey Wolf Optimizer",

author = "Saso Karakatic and Dusan Fister and Beyca, {Omer Faruk} and Iztok Fister",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 21st IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2021 ; Conference date: 18-11-2021 Through 20-11-2021",

year = "2021",

doi = "10.1109/CINTI53070.2021.9668488",

language = "English",

series = "21st IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2021 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "15--20",

booktitle = "21st IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2021 - Proceedings",

address = "United States",

}

Karakatic, S, Fister, D, Beyca, OF & Fister, I 2021, Optimized Class Decomposition for Fault Detection. in 21st IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2021 - Proceedings. 21st IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2021 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 15-20, 21st IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2021, Budapest, Hungary, 18/11/21. https://doi.org/10.1109/CINTI53070.2021.9668488

Optimized Class Decomposition for Fault Detection. / Karakatic, Saso; Fister, Dusan; Beyca, Omer Faruk et al.
21st IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. p. 15-20 (21st IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2021 - Proceedings).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - Optimized Class Decomposition for Fault Detection

AU - Karakatic, Saso

AU - Fister, Dusan

AU - Beyca, Omer Faruk

AU - Fister, Iztok

PY - 2021

Y1 - 2021

N2 - The paper proposes an innovative approach in solving the fault detection problem of sewerage treatment plant machinery. The proposed approach treats the fault detection data with the class decomposition problem, ensuring that a classification algorithm overlooks no disjunct instances. As the class decomposition technique requires heavy customization to each class of instances in every data set, Grey Wolf Optimizer is used to determine the appropriate clustering method with the appropriate setting for each class of instances. The proposed approach is tested on real-life sensor data from a sewerage treatment plant, and the results show that here proposed approach overshadows several manually proposed class decomposition methods.

AB - The paper proposes an innovative approach in solving the fault detection problem of sewerage treatment plant machinery. The proposed approach treats the fault detection data with the class decomposition problem, ensuring that a classification algorithm overlooks no disjunct instances. As the class decomposition technique requires heavy customization to each class of instances in every data set, Grey Wolf Optimizer is used to determine the appropriate clustering method with the appropriate setting for each class of instances. The proposed approach is tested on real-life sensor data from a sewerage treatment plant, and the results show that here proposed approach overshadows several manually proposed class decomposition methods.

KW - Class Decomposition

KW - Clustering

KW - Fault Detection

KW - Grey Wolf Optimizer

UR - http://www.scopus.com/inward/record.url?scp=85124983649&partnerID=8YFLogxK

U2 - 10.1109/CINTI53070.2021.9668488

DO - 10.1109/CINTI53070.2021.9668488

M3 - Conference contribution

AN - SCOPUS:85124983649

T3 - 21st IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2021 - Proceedings

SP - 15

EP - 20

BT - 21st IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2021 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 21st IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2021

Y2 - 18 November 2021 through 20 November 2021

ER -

Karakatic S, Fister D, Beyca OF, Fister I. Optimized Class Decomposition for Fault Detection. In 21st IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2021. p. 15-20. (21st IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2021 - Proceedings). doi: 10.1109/CINTI53070.2021.9668488

Optimized Class Decomposition for Fault Detection

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Finansman

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap