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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| Format: | Conference or Workshop Item |
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2021
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| 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 |
| collection | ePrints |
| 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. |
| first_indexed | 2024-03-05T21:09:40Z |
| format | Conference or Workshop Item |
| id | utm.eprints-96684 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T21:09:40Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | utm.eprints-966842022-08-17T06:48:31Z http://eprints.utm.my/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 |
| 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 |
| title | 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_short | Clustering of maintenance work data for failure mode discrimination |
| title_sort | clustering of maintenance work data for failure mode discrimination |
| topic | T Technology (General) |
| work_keys_str_mv | AT abdullahabdulraniachmed clusteringofmaintenanceworkdataforfailuremodediscrimination AT ajalilsitizura clusteringofmaintenanceworkdataforfailuremodediscrimination AT nikmohamedniknadzirah clusteringofmaintenanceworkdataforfailuremodediscrimination |