Razali, Muhammad Najib and Othman, Siti Hajar and Jamaludin, Ain Farhana and Maimun, Nurul Hana Adi and Jalil, Rohaya Abdul and Mohd Adnan, Yasmin and Zulkarnain, Siti Hafsah (2021) Big data analytics for preventive maintenance management. Planning Malaysia, 19 (3). pp. 423-437. ISSN 1675-6215, DOI https://doi.org/10.21837/PM.V19I17.1019.
Full text not available from this repository.Abstract
Maintenance data for government buildings in Putrajaya, Malaysia, consists of a vast volume of data that is divided into different classes based on the functions of the maintenance tasks. As a result, multiple interactions from stakeholders and customers are required. This necessitates the collection of data that is specific to the stakeholders and customers. Big data can also forecast for predictive maintenance purposes in maintenance management. The current data practise relies solely on well-structured statistical data, resulting in static analysis and findings. Predictive maintenance under the Big Data idea will also use non-visible data such as social media and web search queries, which is a novel way to use Big Data analytics. The metamodel technique will be used in this study to evaluate the predictive maintenance model and faulty events in order to verify that the asset, facilities, and buildings are in excellent working order utilising systematic maintenance analytics. The metamodel method proposed a predictive maintenance procedure in Putrajaya by utilising the big data idea for maintenance management data. © 2021 Malaysian Institute Of Planners. All rights reserved.
| Item Type: | Article |
|---|---|
| Funders: | National Property Research Coordinator (NAPREC) [Grant No: Vot 17H05], Universiti Teknologi Malaysia |
| Uncontrolled Keywords: | Big data; Forecasting; Maintenance; Malaysia |
| Subjects: | H Social Sciences > HA Statistics N Fine Arts > NA Architecture |
| Divisions: | Faculty of the Built Environment |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 09 Oct 2023 05:10 |
| Last Modified: | 09 Oct 2023 09:49 |
| URI: | http://eprints.um.edu.my/id/eprint/35494 |
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