Condition monitoring of broken rotor bars using a hybrid FMM-GA model

Manjeevan Seera; Chee Peng Lim; Chu Kiong Loo

doi:10.1007/978-3-319-12643-2_47

Condition monitoring of broken rotor bars using a hybrid FMM-GA model

Manjeevan Seera, Chee Peng Lim, Chu Kiong Loo

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other › peer-review

1 Citation (Scopus)

Abstract

A condition monitoring system for induction motors using a hybrid Fuzzy Min-Max (FMM) neural network and Genetic Algorithm (GA) is presented in this paper. Two types of experiments, one from the finite element method and another from real laboratory tests of broken rotor bars in an induction motor are conducted. The induction motor with broken rotor bars is operated under different load conditions. FMM is first used for learning and distinguishing between a healthy motor and one with broken rotor bars. The GA is then utilized for extracting fuzzy if-then rules using the don’t care approach in minimizing the number of rules. The results clearly demonstrate the effectiveness of the hybrid FMM-GA model in condition monitoring of broken rotor bars in induction motors.

Original language	English
Title of host publication	Neural Information Processing - 21st International Conference, ICONIP 2014, Proceedings
Editors	Chu Kiong Loo, Keem Siah Yap, Kok Wai Wong, Andrew Teoh, Kaizhu Huang
Publisher	Springer
Pages	381-389
Number of pages	9
ISBN (Electronic)	9783319126425
DOIs	https://doi.org/10.1007/978-3-319-12643-2_47
Publication status	Published - 2014
Externally published	Yes
Event	International Conference on Neural Information Processing 2014 - Kuching, Malaysia Duration: 3 Nov 2014 → 6 Nov 2014 Conference number: 21st https://link.springer.com/book/10.1007/978-3-319-12637-1

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8836
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Neural Information Processing 2014
Abbreviated title	ICONIP 2014
Country/Territory	Malaysia
City	Kuching
Period	3/11/14 → 6/11/14
Internet address	https://link.springer.com/book/10.1007/978-3-319-12637-1

Keywords

Condition monitoring
Fault diagnosis
Fuzzy min-max neural network
Genetic algorithms
Induction motor

Access to Document

10.1007/978-3-319-12643-2_47

Cite this

Seera, M., Lim, C. P., & Loo, C. K. (2014). Condition monitoring of broken rotor bars using a hybrid FMM-GA model. In C. K. Loo, K. S. Yap, K. W. Wong, A. Teoh, & K. Huang (Eds.), Neural Information Processing - 21st International Conference, ICONIP 2014, Proceedings (pp. 381-389). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8836). Springer. https://doi.org/10.1007/978-3-319-12643-2_47

Seera, Manjeevan ; Lim, Chee Peng ; Loo, Chu Kiong. / Condition monitoring of broken rotor bars using a hybrid FMM-GA model. Neural Information Processing - 21st International Conference, ICONIP 2014, Proceedings. editor / Chu Kiong Loo ; Keem Siah Yap ; Kok Wai Wong ; Andrew Teoh ; Kaizhu Huang. Springer, 2014. pp. 381-389 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{2aff0dc525974cfd95f21a3f80bf2e24,

title = "Condition monitoring of broken rotor bars using a hybrid FMM-GA model",

abstract = "A condition monitoring system for induction motors using a hybrid Fuzzy Min-Max (FMM) neural network and Genetic Algorithm (GA) is presented in this paper. Two types of experiments, one from the finite element method and another from real laboratory tests of broken rotor bars in an induction motor are conducted. The induction motor with broken rotor bars is operated under different load conditions. FMM is first used for learning and distinguishing between a healthy motor and one with broken rotor bars. The GA is then utilized for extracting fuzzy if-then rules using the don{\textquoteright}t care approach in minimizing the number of rules. The results clearly demonstrate the effectiveness of the hybrid FMM-GA model in condition monitoring of broken rotor bars in induction motors.",

keywords = "Condition monitoring, Fault diagnosis, Fuzzy min-max neural network, Genetic algorithms, Induction motor",

author = "Manjeevan Seera and Lim, \{Chee Peng\} and Loo, \{Chu Kiong\}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; International Conference on Neural Information Processing 2014, ICONIP 2014 ; Conference date: 03-11-2014 Through 06-11-2014",

year = "2014",

doi = "10.1007/978-3-319-12643-2\_47",

language = "English",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "381--389",

editor = "Loo, \{Chu Kiong\} and Yap, \{Keem Siah\} and Wong, \{Kok Wai\} and Andrew Teoh and Kaizhu Huang",

booktitle = "Neural Information Processing - 21st International Conference, ICONIP 2014, Proceedings",

address = "Switzerland",

url = "https://link.springer.com/book/10.1007/978-3-319-12637-1",

}

Seera, M, Lim, CP & Loo, CK 2014, Condition monitoring of broken rotor bars using a hybrid FMM-GA model. in CK Loo, KS Yap, KW Wong, A Teoh & K Huang (eds), Neural Information Processing - 21st International Conference, ICONIP 2014, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8836, Springer, pp. 381-389, International Conference on Neural Information Processing 2014, Kuching, Malaysia, 3/11/14. https://doi.org/10.1007/978-3-319-12643-2_47

Condition monitoring of broken rotor bars using a hybrid FMM-GA model. / Seera, Manjeevan; Lim, Chee Peng; Loo, Chu Kiong.
Neural Information Processing - 21st International Conference, ICONIP 2014, Proceedings. ed. / Chu Kiong Loo; Keem Siah Yap; Kok Wai Wong; Andrew Teoh; Kaizhu Huang. Springer, 2014. p. 381-389 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8836).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other › peer-review

TY - GEN

T1 - Condition monitoring of broken rotor bars using a hybrid FMM-GA model

AU - Seera, Manjeevan

AU - Lim, Chee Peng

AU - Loo, Chu Kiong

N1 - Conference code: 21st

PY - 2014

Y1 - 2014

N2 - A condition monitoring system for induction motors using a hybrid Fuzzy Min-Max (FMM) neural network and Genetic Algorithm (GA) is presented in this paper. Two types of experiments, one from the finite element method and another from real laboratory tests of broken rotor bars in an induction motor are conducted. The induction motor with broken rotor bars is operated under different load conditions. FMM is first used for learning and distinguishing between a healthy motor and one with broken rotor bars. The GA is then utilized for extracting fuzzy if-then rules using the don’t care approach in minimizing the number of rules. The results clearly demonstrate the effectiveness of the hybrid FMM-GA model in condition monitoring of broken rotor bars in induction motors.

AB - A condition monitoring system for induction motors using a hybrid Fuzzy Min-Max (FMM) neural network and Genetic Algorithm (GA) is presented in this paper. Two types of experiments, one from the finite element method and another from real laboratory tests of broken rotor bars in an induction motor are conducted. The induction motor with broken rotor bars is operated under different load conditions. FMM is first used for learning and distinguishing between a healthy motor and one with broken rotor bars. The GA is then utilized for extracting fuzzy if-then rules using the don’t care approach in minimizing the number of rules. The results clearly demonstrate the effectiveness of the hybrid FMM-GA model in condition monitoring of broken rotor bars in induction motors.

KW - Condition monitoring

KW - Fault diagnosis

KW - Fuzzy min-max neural network

KW - Genetic algorithms

KW - Induction motor

UR - http://www.scopus.com/inward/record.url?scp=84910001574&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-12643-2_47

DO - 10.1007/978-3-319-12643-2_47

M3 - Conference Paper

AN - SCOPUS:84910001574

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 381

EP - 389

BT - Neural Information Processing - 21st International Conference, ICONIP 2014, Proceedings

A2 - Loo, Chu Kiong

A2 - Yap, Keem Siah

A2 - Wong, Kok Wai

A2 - Teoh, Andrew

A2 - Huang, Kaizhu

PB - Springer

T2 - International Conference on Neural Information Processing 2014

Y2 - 3 November 2014 through 6 November 2014

ER -

Seera M, Lim CP, Loo CK. Condition monitoring of broken rotor bars using a hybrid FMM-GA model. In Loo CK, Yap KS, Wong KW, Teoh A, Huang K, editors, Neural Information Processing - 21st International Conference, ICONIP 2014, Proceedings. Springer. 2014. p. 381-389. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-12643-2_47

Condition monitoring of broken rotor bars using a hybrid FMM-GA model

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Cite this