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A novel hybrid extreme learning machine-whale optimization algorithm for bearing fault diagnosis

In order to avoid fatalities and ensure safe operation, a good and accurate diagnosis method is required. A diagnosis method based on extreme learning machine (ELM) has attracted much attention and the ELM method had been applied in various field of study. The advantages of the ELM method which are...

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Main Authors: Firdaus Isham, M., Saufi, M. S. R., A. R., Amirul
Format: Conference or Workshop Item
Published: 2022
Subjects:
TJ Mechanical engineering and machinery
Online Access:http://eprints.utm.my/id/eprint/99371/
http://dx.doi.org/10.1007/978-981-16-8690-0_55
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spelling my.utm.993712023-02-23T04:09:03Z http://eprints.utm.my/id/eprint/99371/ A novel hybrid extreme learning machine-whale optimization algorithm for bearing fault diagnosis Firdaus Isham, M. Saufi, M. S. R. A. R., Amirul TJ Mechanical engineering and machinery In order to avoid fatalities and ensure safe operation, a good and accurate diagnosis method is required. A diagnosis method based on extreme learning machine (ELM) has attracted much attention and the ELM method had been applied in various field of study. The advantages of the ELM method which are rapid learning rate, better generalization performance and ease of implementation makes the ELM method suitable to be used in various field including fault diagnosis fields. However, the performance of the ELM method becomes inefficient due to incorrect selection of neurons number and randomness of input weight and hidden layer bias. Hence, this paper aims to propose a novel hybrid fault diagnosis method based on ELM and whale optimization algorithm (WOA), known as ELM-WOA for bearing fault diagnosis. Four different types of bearing datasets from Case Western Reserve University Bearing Data Centre were used in this paper in order to present the performance of the proposed method. Based on the result, the performance of the proposed method was able to surpass the performance of the conventional ELM. 2022 Conference or Workshop Item PeerReviewed Firdaus Isham, M. and Saufi, M. S. R. and A. R., Amirul (2022) A novel hybrid extreme learning machine-whale optimization algorithm for bearing fault diagnosis. In: 6th International Conference on Electrical, Control and Computer Engineering, InECCE 2021, 23rd August 2021, Kuantan, Pahang. http://dx.doi.org/10.1007/978-981-16-8690-0_55
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Firdaus Isham, M.
Saufi, M. S. R.
A. R., Amirul
A novel hybrid extreme learning machine-whale optimization algorithm for bearing fault diagnosis
description In order to avoid fatalities and ensure safe operation, a good and accurate diagnosis method is required. A diagnosis method based on extreme learning machine (ELM) has attracted much attention and the ELM method had been applied in various field of study. The advantages of the ELM method which are rapid learning rate, better generalization performance and ease of implementation makes the ELM method suitable to be used in various field including fault diagnosis fields. However, the performance of the ELM method becomes inefficient due to incorrect selection of neurons number and randomness of input weight and hidden layer bias. Hence, this paper aims to propose a novel hybrid fault diagnosis method based on ELM and whale optimization algorithm (WOA), known as ELM-WOA for bearing fault diagnosis. Four different types of bearing datasets from Case Western Reserve University Bearing Data Centre were used in this paper in order to present the performance of the proposed method. Based on the result, the performance of the proposed method was able to surpass the performance of the conventional ELM.
format Conference or Workshop Item
author Firdaus Isham, M.
Saufi, M. S. R.
A. R., Amirul
author_facet Firdaus Isham, M.
Saufi, M. S. R.
A. R., Amirul
author_sort Firdaus Isham, M.
title A novel hybrid extreme learning machine-whale optimization algorithm for bearing fault diagnosis
title_short A novel hybrid extreme learning machine-whale optimization algorithm for bearing fault diagnosis
title_full A novel hybrid extreme learning machine-whale optimization algorithm for bearing fault diagnosis
title_fullStr A novel hybrid extreme learning machine-whale optimization algorithm for bearing fault diagnosis
title_full_unstemmed A novel hybrid extreme learning machine-whale optimization algorithm for bearing fault diagnosis
title_sort novel hybrid extreme learning machine-whale optimization algorithm for bearing fault diagnosis
publishDate 2022
url http://eprints.utm.my/id/eprint/99371/
http://dx.doi.org/10.1007/978-981-16-8690-0_55
_version_ 1758950352242081792
score 13.244404

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