Multi-core accelerated discriminant feature selection for real-time bearing fault diagnosis

Md Rashedul Islam; Md Sharif Uddin; Sheraz Khan; Jong Myon Kim; Cheol Hong Kim

doi:10.1007/978-3-319-42007-3_56

Multi-core accelerated discriminant feature selection for real-time bearing fault diagnosis

Md Rashedul Islam, Md Sharif Uddin, Sheraz Khan, Jong Myon Kim, Cheol Hong Kim

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

3 Scopus citations

Abstract

This paper presents a real-time and reliable bearing fault diagnosis scheme for induction motors with optimal fault feature distribution analysis based discriminant feature selection. The sequential forward selection (SFS) with the proposed feature evaluation function is used to select the discriminative feature vector. Then, the k-nearest neighbor (k-NN) is employed to diagnose unknown fault signals and validate the effectiveness of the proposed feature selection and fault diagnosis model. However, the process of feature vector evaluation for feature selection is computationally expensive. This paper presents a parallel implementation of feature selection with a feature evaluation algorithm on a multi-core architecture to accelerate the algorithm. The optimal organization of processing elements (PE) and the proper distribution of feature data into memory of each PE improve diagnosis performance and reduce computational time to meet real-time fault diagnosis.

Original language	English
Title of host publication	Trends in Applied Knowledge-Based Systems and Data Science - 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Proceedings
Editors	Moonis Ali, Hamido Fujita, Jun Sasaki, Masaki Kurematsu, Ali Selamat
Publisher	Springer Verlag
Pages	645-656
Number of pages	12
ISBN (Print)	9783319420066
DOIs	https://doi.org/10.1007/978-3-319-42007-3_56
State	Published - 2016
Externally published	Yes
Event	29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016 - Morioka, Japan Duration: 2 Aug 2016 → 4 Aug 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9799
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016
Country/Territory	Japan
City	Morioka
Period	2/08/16 → 4/08/16

Keywords

Class compactness
Class separability
Fault diagnosis
Feature selection
Multi-core architecture

Access to Document

10.1007/978-3-319-42007-3_56

Cite this

Rashedul Islam, M., Sharif Uddin, M., Khan, S., Kim, J. M., & Kim, C. H. (2016). Multi-core accelerated discriminant feature selection for real-time bearing fault diagnosis. In M. Ali, H. Fujita, J. Sasaki, M. Kurematsu, & A. Selamat (Eds.), Trends in Applied Knowledge-Based Systems and Data Science - 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Proceedings (pp. 645-656). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9799). Springer Verlag. https://doi.org/10.1007/978-3-319-42007-3_56

Rashedul Islam, Md ; Sharif Uddin, Md ; Khan, Sheraz et al. / Multi-core accelerated discriminant feature selection for real-time bearing fault diagnosis. Trends in Applied Knowledge-Based Systems and Data Science - 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Proceedings. editor / Moonis Ali ; Hamido Fujita ; Jun Sasaki ; Masaki Kurematsu ; Ali Selamat. Springer Verlag, 2016. pp. 645-656 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Multi-core accelerated discriminant feature selection for real-time bearing fault diagnosis",

abstract = "This paper presents a real-time and reliable bearing fault diagnosis scheme for induction motors with optimal fault feature distribution analysis based discriminant feature selection. The sequential forward selection (SFS) with the proposed feature evaluation function is used to select the discriminative feature vector. Then, the k-nearest neighbor (k-NN) is employed to diagnose unknown fault signals and validate the effectiveness of the proposed feature selection and fault diagnosis model. However, the process of feature vector evaluation for feature selection is computationally expensive. This paper presents a parallel implementation of feature selection with a feature evaluation algorithm on a multi-core architecture to accelerate the algorithm. The optimal organization of processing elements (PE) and the proper distribution of feature data into memory of each PE improve diagnosis performance and reduce computational time to meet real-time fault diagnosis.",

keywords = "Class compactness, Class separability, Fault diagnosis, Feature selection, Multi-core architecture",

author = "\{Rashedul Islam\}, Md and \{Sharif Uddin\}, Md and Sheraz Khan and Kim, \{Jong Myon\} and Kim, \{Cheol Hong\}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016 ; Conference date: 02-08-2016 Through 04-08-2016",

year = "2016",

doi = "10.1007/978-3-319-42007-3\_56",

language = "English",

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editor = "Moonis Ali and Hamido Fujita and Jun Sasaki and Masaki Kurematsu and Ali Selamat",

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Rashedul Islam, M, Sharif Uddin, M, Khan, S, Kim, JM & Kim, CH 2016, Multi-core accelerated discriminant feature selection for real-time bearing fault diagnosis. in M Ali, H Fujita, J Sasaki, M Kurematsu & A Selamat (eds), Trends in Applied Knowledge-Based Systems and Data Science - 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9799, Springer Verlag, pp. 645-656, 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Morioka, Japan, 2/08/16. https://doi.org/10.1007/978-3-319-42007-3_56

Multi-core accelerated discriminant feature selection for real-time bearing fault diagnosis. / Rashedul Islam, Md; Sharif Uddin, Md; Khan, Sheraz et al.
Trends in Applied Knowledge-Based Systems and Data Science - 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Proceedings. ed. / Moonis Ali; Hamido Fujita; Jun Sasaki; Masaki Kurematsu; Ali Selamat. Springer Verlag, 2016. p. 645-656 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9799).

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

TY - GEN

T1 - Multi-core accelerated discriminant feature selection for real-time bearing fault diagnosis

AU - Rashedul Islam, Md

AU - Sharif Uddin, Md

AU - Khan, Sheraz

AU - Kim, Jong Myon

AU - Kim, Cheol Hong

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2016.

PY - 2016

Y1 - 2016

N2 - This paper presents a real-time and reliable bearing fault diagnosis scheme for induction motors with optimal fault feature distribution analysis based discriminant feature selection. The sequential forward selection (SFS) with the proposed feature evaluation function is used to select the discriminative feature vector. Then, the k-nearest neighbor (k-NN) is employed to diagnose unknown fault signals and validate the effectiveness of the proposed feature selection and fault diagnosis model. However, the process of feature vector evaluation for feature selection is computationally expensive. This paper presents a parallel implementation of feature selection with a feature evaluation algorithm on a multi-core architecture to accelerate the algorithm. The optimal organization of processing elements (PE) and the proper distribution of feature data into memory of each PE improve diagnosis performance and reduce computational time to meet real-time fault diagnosis.

AB - This paper presents a real-time and reliable bearing fault diagnosis scheme for induction motors with optimal fault feature distribution analysis based discriminant feature selection. The sequential forward selection (SFS) with the proposed feature evaluation function is used to select the discriminative feature vector. Then, the k-nearest neighbor (k-NN) is employed to diagnose unknown fault signals and validate the effectiveness of the proposed feature selection and fault diagnosis model. However, the process of feature vector evaluation for feature selection is computationally expensive. This paper presents a parallel implementation of feature selection with a feature evaluation algorithm on a multi-core architecture to accelerate the algorithm. The optimal organization of processing elements (PE) and the proper distribution of feature data into memory of each PE improve diagnosis performance and reduce computational time to meet real-time fault diagnosis.

KW - Class compactness

KW - Class separability

KW - Fault diagnosis

KW - Feature selection

KW - Multi-core architecture

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SN - 9783319420066

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

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EP - 656

BT - Trends in Applied Knowledge-Based Systems and Data Science - 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Proceedings

A2 - Ali, Moonis

A2 - Fujita, Hamido

A2 - Sasaki, Jun

A2 - Kurematsu, Masaki

A2 - Selamat, Ali

PB - Springer Verlag

T2 - 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016

Y2 - 2 August 2016 through 4 August 2016

ER -

Rashedul Islam M, Sharif Uddin M, Khan S, Kim JM, Kim CH. Multi-core accelerated discriminant feature selection for real-time bearing fault diagnosis. In Ali M, Fujita H, Sasaki J, Kurematsu M, Selamat A, editors, Trends in Applied Knowledge-Based Systems and Data Science - 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Proceedings. Springer Verlag. 2016. p. 645-656. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-42007-3_56

Multi-core accelerated discriminant feature selection for real-time bearing fault diagnosis

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Keywords

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