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Clustering of maintenance work data for failure mode discrimination

A fast and efficient method to discriminate failure modes from maintenance work orders will facilitate and motivate proactive maintenance development. This paper aims to propose a faster and as efficient clustering methodology that differs from previous text mining attempts. Text mining attempts are...

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Main Authors: Abdullah, Abdul Rani Achmed, A. Jalil, Siti Zura, Nik Mohamed, Nik Nadzirah
Format: Conference or Workshop Item
Published: 2021
Subjects:
T Technology (General)
Online Access:http://eprints.utm.my/id/eprint/96684/
https://www.ieomsociety.org/proceedings/2021india/68.pdf
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id my.utm.96684
record_format eprints
spelling my.utm.966842022-08-17T06:48:31Z http://eprints.utm.my/id/eprint/96684/ Clustering of maintenance work data for failure mode discrimination Abdullah, Abdul Rani Achmed A. Jalil, Siti Zura Nik Mohamed, Nik Nadzirah T Technology (General) A fast and efficient method to discriminate failure modes from maintenance work orders will facilitate and motivate proactive maintenance development. This paper aims to propose a faster and as efficient clustering methodology that differs from previous text mining attempts. Text mining attempts are very dependent on correctly classifying text but the method proposed here is text independent. It is based on time to repair (TTR), time before failure (TBF) and other available identifiers. Using K-means as the clustering algorithm, the processing speed was greatly reduced. Singularity of discriminated failure modes were as good as previous text mining attempts. 2021 Conference or Workshop Item PeerReviewed Abdullah, Abdul Rani Achmed and A. Jalil, Siti Zura and Nik Mohamed, Nik Nadzirah (2021) Clustering of maintenance work data for failure mode discrimination. In: 1st Indian International Conference on Industrial Engineering and Operations Management, IEOM 2021, 16 - 18 August 2021, Virtual, Online. https://www.ieomsociety.org/proceedings/2021india/68.pdf
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 T Technology (General)
spellingShingle T Technology (General)
Abdullah, Abdul Rani Achmed
A. Jalil, Siti Zura
Nik Mohamed, Nik Nadzirah
Clustering of maintenance work data for failure mode discrimination
description A fast and efficient method to discriminate failure modes from maintenance work orders will facilitate and motivate proactive maintenance development. This paper aims to propose a faster and as efficient clustering methodology that differs from previous text mining attempts. Text mining attempts are very dependent on correctly classifying text but the method proposed here is text independent. It is based on time to repair (TTR), time before failure (TBF) and other available identifiers. Using K-means as the clustering algorithm, the processing speed was greatly reduced. Singularity of discriminated failure modes were as good as previous text mining attempts.
format Conference or Workshop Item
author Abdullah, Abdul Rani Achmed
A. Jalil, Siti Zura
Nik Mohamed, Nik Nadzirah
author_facet Abdullah, Abdul Rani Achmed
A. Jalil, Siti Zura
Nik Mohamed, Nik Nadzirah
author_sort Abdullah, Abdul Rani Achmed
title Clustering of maintenance work data for failure mode discrimination
title_short Clustering of maintenance work data for failure mode discrimination
title_full Clustering of maintenance work data for failure mode discrimination
title_fullStr Clustering of maintenance work data for failure mode discrimination
title_full_unstemmed Clustering of maintenance work data for failure mode discrimination
title_sort clustering of maintenance work data for failure mode discrimination
publishDate 2021
url http://eprints.utm.my/id/eprint/96684/
https://www.ieomsociety.org/proceedings/2021india/68.pdf
_version_ 1743107013649367040
score 13.211869

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