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Bearing fault diagnosis using deep sparse autoencoder

Rolling element bearing is an important component in various machinery. Faulty on bearing cause severe equipment damage that lead to high maintenance cost. The development of deep learning has been paid a considerable amount of attention to fault diagnosis on rolling element bearing. Traditional mac...

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Main Authors: Saufi, S. R., Ahmad, Z. A. B., Leong, M. S., Hee, L. M.
Format: Conference or Workshop Item
Language:English
Published: 2021
Subjects:
TJ Mechanical engineering and machinery
Online Access:http://eprints.utm.my/id/eprint/94188/1/SRSaufi2021_BearingFaultDiagnosisUsingDeepSparseAutoencoder.pdf
http://eprints.utm.my/id/eprint/94188/
http://dx.doi.org/10.1088/1757-899X/1062/1/012002
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spelling my.utm.941882022-02-28T13:25:14Z http://eprints.utm.my/id/eprint/94188/ Bearing fault diagnosis using deep sparse autoencoder Saufi, S. R. Ahmad, Z. A. B. Leong, M. S. Hee, L. M. TJ Mechanical engineering and machinery Rolling element bearing is an important component in various machinery. Faulty on bearing cause severe equipment damage that lead to high maintenance cost. The development of deep learning has been paid a considerable amount of attention to fault diagnosis on rolling element bearing. Traditional machine learning such as Artificial Neural Network and Support Vector Machine have problems of lacking expression capacity, existing the curse of dimensionality, require manual feature extraction and require an additional feature selection. Deep learning model has the ability to effectively mine the high dimensional features and accurately recognize the health condition. In consequence, deep learning model has turned into an innovative and promising research in bearing fault diagnosis field. Thus, this paper tends to proposed Deep Sparse Autoencoder (DSAE) with Teager Kaiser Energy Operator (TKEO) to diagnose the bearing condition. DSAE is one of deep learning model which uses the architecture of neural network. During the analysis, the hyperparameter of DSAE model was optimized by Ant Lion Optimization. The analysis results show that the proposed TKEO-DSAE achieved 99.5% accuracy of the fault diagnosis. The comparative study between proposed model and ANN proved that deep learning model outperform traditional machine learning model on bearing fault diagnosis. 2021 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/94188/1/SRSaufi2021_BearingFaultDiagnosisUsingDeepSparseAutoencoder.pdf Saufi, S. R. and Ahmad, Z. A. B. and Leong, M. S. and Hee, L. M. (2021) Bearing fault diagnosis using deep sparse autoencoder. In: 1st International Colloquium on Computational and Experimental Mechanics, ICCEM 2020, 25 - 26 June 2020, Selangor, Malaysia. http://dx.doi.org/10.1088/1757-899X/1062/1/012002
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/
language English
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Saufi, S. R.
Ahmad, Z. A. B.
Leong, M. S.
Hee, L. M.
Bearing fault diagnosis using deep sparse autoencoder
description Rolling element bearing is an important component in various machinery. Faulty on bearing cause severe equipment damage that lead to high maintenance cost. The development of deep learning has been paid a considerable amount of attention to fault diagnosis on rolling element bearing. Traditional machine learning such as Artificial Neural Network and Support Vector Machine have problems of lacking expression capacity, existing the curse of dimensionality, require manual feature extraction and require an additional feature selection. Deep learning model has the ability to effectively mine the high dimensional features and accurately recognize the health condition. In consequence, deep learning model has turned into an innovative and promising research in bearing fault diagnosis field. Thus, this paper tends to proposed Deep Sparse Autoencoder (DSAE) with Teager Kaiser Energy Operator (TKEO) to diagnose the bearing condition. DSAE is one of deep learning model which uses the architecture of neural network. During the analysis, the hyperparameter of DSAE model was optimized by Ant Lion Optimization. The analysis results show that the proposed TKEO-DSAE achieved 99.5% accuracy of the fault diagnosis. The comparative study between proposed model and ANN proved that deep learning model outperform traditional machine learning model on bearing fault diagnosis.
format Conference or Workshop Item
author Saufi, S. R.
Ahmad, Z. A. B.
Leong, M. S.
Hee, L. M.
author_facet Saufi, S. R.
Ahmad, Z. A. B.
Leong, M. S.
Hee, L. M.
author_sort Saufi, S. R.
title Bearing fault diagnosis using deep sparse autoencoder
title_short Bearing fault diagnosis using deep sparse autoencoder
title_full Bearing fault diagnosis using deep sparse autoencoder
title_fullStr Bearing fault diagnosis using deep sparse autoencoder
title_full_unstemmed Bearing fault diagnosis using deep sparse autoencoder
title_sort bearing fault diagnosis using deep sparse autoencoder
publishDate 2021
url http://eprints.utm.my/id/eprint/94188/1/SRSaufi2021_BearingFaultDiagnosisUsingDeepSparseAutoencoder.pdf
http://eprints.utm.my/id/eprint/94188/
http://dx.doi.org/10.1088/1757-899X/1062/1/012002
_version_ 1726791494434553856
score 13.244414

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