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Neural network prognostics model for industrial equipment maintenance

This paper presents a new prognostics model based on neural network technique for supporting industrial maintenance decision. In this study, the probabilities of failure based on the real condition equipment are initially calculated by using logistic regression method. The failure probabilities are...

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Main Authors: Asmai, Siti Azirah, Hasan Basari, Abd Samad, Shibghatullah, Abdul Samad, Ibrahim, Nuzulha Khilwani, Hussin, Burairah
Format: Conference or Workshop Item
Language:English
Published: 2011
Subjects:
T Technology (General)
TJ Mechanical engineering and machinery
Online Access:http://eprints.utem.edu.my/id/eprint/20150/1/NEURAL%20NETWORK%20PROGNOSTICS%20MODEL%20FOR%20INDUSTRIAL%20EQUIPMENT%20MAINTENANCE-SITI%20AZIRAH%20ASMAI-MAK%2000292%20RAF.pdf
http://eprints.utem.edu.my/id/eprint/20150/
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spelling my.utem.eprints.201502023-06-15T10:11:07Z http://eprints.utem.edu.my/id/eprint/20150/ Neural network prognostics model for industrial equipment maintenance Asmai, Siti Azirah Hasan Basari, Abd Samad Shibghatullah, Abdul Samad Ibrahim, Nuzulha Khilwani Hussin, Burairah T Technology (General) TJ Mechanical engineering and machinery This paper presents a new prognostics model based on neural network technique for supporting industrial maintenance decision. In this study, the probabilities of failure based on the real condition equipment are initially calculated by using logistic regression method. The failure probabilities are subsequently utilized as input for prognostics model to predict the future value of failure condition and then used to estimate remaining useful lifetime of equipment, by having a time series of predicted failure probability, the failure distribution can be generated and used in the maintenance cost model to decide the optimal time to do maintenance. The proposed prognostic model is implemented in the industrial equipment known as autoclave burner. The result from the model reveals that it can give prior warnings and indication to the maintenance department to take an appropriate decision instead of dealing with the failures while the autoclave burner is still operating. This significant contribution provides new insights into the maintenance strategy which enables the use of existing condition data from industrial equipment and prognostics approach 2011 Conference or Workshop Item PeerReviewed text en http://eprints.utem.edu.my/id/eprint/20150/1/NEURAL%20NETWORK%20PROGNOSTICS%20MODEL%20FOR%20INDUSTRIAL%20EQUIPMENT%20MAINTENANCE-SITI%20AZIRAH%20ASMAI-MAK%2000292%20RAF.pdf Asmai, Siti Azirah and Hasan Basari, Abd Samad and Shibghatullah, Abdul Samad and Ibrahim, Nuzulha Khilwani and Hussin, Burairah (2011) Neural network prognostics model for industrial equipment maintenance. In: 11th International Conference on Hybrid Intelligent Systems, HIS 2011 , 5-8 December 2011, Melaka. (Submitted)
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
topic T Technology (General)
TJ Mechanical engineering and machinery
spellingShingle T Technology (General)
TJ Mechanical engineering and machinery
Asmai, Siti Azirah
Hasan Basari, Abd Samad
Shibghatullah, Abdul Samad
Ibrahim, Nuzulha Khilwani
Hussin, Burairah
Neural network prognostics model for industrial equipment maintenance
description This paper presents a new prognostics model based on neural network technique for supporting industrial maintenance decision. In this study, the probabilities of failure based on the real condition equipment are initially calculated by using logistic regression method. The failure probabilities are subsequently utilized as input for prognostics model to predict the future value of failure condition and then used to estimate remaining useful lifetime of equipment, by having a time series of predicted failure probability, the failure distribution can be generated and used in the maintenance cost model to decide the optimal time to do maintenance. The proposed prognostic model is implemented in the industrial equipment known as autoclave burner. The result from the model reveals that it can give prior warnings and indication to the maintenance department to take an appropriate decision instead of dealing with the failures while the autoclave burner is still operating. This significant contribution provides new insights into the maintenance strategy which enables the use of existing condition data from industrial equipment and prognostics approach
format Conference or Workshop Item
author Asmai, Siti Azirah
Hasan Basari, Abd Samad
Shibghatullah, Abdul Samad
Ibrahim, Nuzulha Khilwani
Hussin, Burairah
author_facet Asmai, Siti Azirah
Hasan Basari, Abd Samad
Shibghatullah, Abdul Samad
Ibrahim, Nuzulha Khilwani
Hussin, Burairah
author_sort Asmai, Siti Azirah
title Neural network prognostics model for industrial equipment maintenance
title_short Neural network prognostics model for industrial equipment maintenance
title_full Neural network prognostics model for industrial equipment maintenance
title_fullStr Neural network prognostics model for industrial equipment maintenance
title_full_unstemmed Neural network prognostics model for industrial equipment maintenance
title_sort neural network prognostics model for industrial equipment maintenance
publishDate 2011
url http://eprints.utem.edu.my/id/eprint/20150/1/NEURAL%20NETWORK%20PROGNOSTICS%20MODEL%20FOR%20INDUSTRIAL%20EQUIPMENT%20MAINTENANCE-SITI%20AZIRAH%20ASMAI-MAK%2000292%20RAF.pdf
http://eprints.utem.edu.my/id/eprint/20150/
_version_ 1769847391608897536
score 13.211869

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