Prediction of Maintenance Activities Using Generalized Sequential Pattern and Association Rules in Data Mining
This study proposed a data mining framework for predicting sequential patterns of maintenance activities. The framework consisted of data collection, prediction of maintenance activities with and without attributes, and then the comparison between prediction results. In data collection, historical d...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/13/4/946 |
| _version_ | 1797606158410711040 |
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| author | Abbas Al-Refaie Banan Abu Hamdieh Natalija Lepkova |
| author_facet | Abbas Al-Refaie Banan Abu Hamdieh Natalija Lepkova |
| author_sort | Abbas Al-Refaie |
| collection | DOAJ |
| description | This study proposed a data mining framework for predicting sequential patterns of maintenance activities. The framework consisted of data collection, prediction of maintenance activities with and without attributes, and then the comparison between prediction results. In data collection, historical data were collected regarding maintenance activities and product attributes. The generalized sequential pattern (GSP) and association rules were then applied to predict maintenance activities with and without attributes to determine the frequent sequential patterns and significant rules of maintenance activities. Finally, a comparison was performed between the sequences of maintenance activities with and without attributes. A real case study of washing machine products was presented to illustrate the developed framework. The results showed that the proposed framework effectively predicted the next maintenance activities and planning preventive maintenance based on product attributes. In conclusion, the data mining approach is found effective in determining the maintenance sequence that reduces downtime and thereby enhancing productivity and availability. |
| first_indexed | 2024-03-11T05:11:20Z |
| format | Article |
| id | doaj.art-b2631c60f20d4cff9fd373bc7c5e8587 |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-11T05:11:20Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-b2631c60f20d4cff9fd373bc7c5e85872023-11-17T18:35:21ZengMDPI AGBuildings2075-53092023-04-0113494610.3390/buildings13040946Prediction of Maintenance Activities Using Generalized Sequential Pattern and Association Rules in Data MiningAbbas Al-Refaie0Banan Abu Hamdieh1Natalija Lepkova2Department of Industrial Engineering, The University of Jordan, Amman 11942, JordanDepartment of Industrial Engineering, The University of Jordan, Amman 11942, JordanDepartment of Construction Management and Real Estate, Faculty of Civil Engineering, Vilnius Gediminas Technical University, Sauletekio Av. 11, 10223 Vilnius, LithuaniaThis study proposed a data mining framework for predicting sequential patterns of maintenance activities. The framework consisted of data collection, prediction of maintenance activities with and without attributes, and then the comparison between prediction results. In data collection, historical data were collected regarding maintenance activities and product attributes. The generalized sequential pattern (GSP) and association rules were then applied to predict maintenance activities with and without attributes to determine the frequent sequential patterns and significant rules of maintenance activities. Finally, a comparison was performed between the sequences of maintenance activities with and without attributes. A real case study of washing machine products was presented to illustrate the developed framework. The results showed that the proposed framework effectively predicted the next maintenance activities and planning preventive maintenance based on product attributes. In conclusion, the data mining approach is found effective in determining the maintenance sequence that reduces downtime and thereby enhancing productivity and availability.https://www.mdpi.com/2075-5309/13/4/946prediction of maintenancedata mininggeneralized sequential patternassociation rule miningmaintenance planning |
| spellingShingle | Abbas Al-Refaie Banan Abu Hamdieh Natalija Lepkova Prediction of Maintenance Activities Using Generalized Sequential Pattern and Association Rules in Data Mining Buildings prediction of maintenance data mining generalized sequential pattern association rule mining maintenance planning |
| title | Prediction of Maintenance Activities Using Generalized Sequential Pattern and Association Rules in Data Mining |
| title_full | Prediction of Maintenance Activities Using Generalized Sequential Pattern and Association Rules in Data Mining |
| title_fullStr | Prediction of Maintenance Activities Using Generalized Sequential Pattern and Association Rules in Data Mining |
| title_full_unstemmed | Prediction of Maintenance Activities Using Generalized Sequential Pattern and Association Rules in Data Mining |
| title_short | Prediction of Maintenance Activities Using Generalized Sequential Pattern and Association Rules in Data Mining |
| title_sort | prediction of maintenance activities using generalized sequential pattern and association rules in data mining |
| topic | prediction of maintenance data mining generalized sequential pattern association rule mining maintenance planning |
| url | https://www.mdpi.com/2075-5309/13/4/946 |
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