Predictive Maintenance 4.0 for Chilled Water System at Commercial Buildings: A Methodological Framework
Predictive maintenance is considered as one of the most important strategies for managing the utility systems of commercial buildings. This research focused on chilled water system (CWS) components and proposed a methodological framework to build a comprehensive predictive maintenance program in lin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/13/2/497 |
| _version_ | 1797621929812688896 |
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| author | Malek Almobarek Kepa Mendibil Abdalla Alrashdan |
| author_facet | Malek Almobarek Kepa Mendibil Abdalla Alrashdan |
| author_sort | Malek Almobarek |
| collection | DOAJ |
| description | Predictive maintenance is considered as one of the most important strategies for managing the utility systems of commercial buildings. This research focused on chilled water system (CWS) components and proposed a methodological framework to build a comprehensive predictive maintenance program in line with Industry 4.0/Quality 4.0 (PdM 4.0). This research followed a systematic literature review (SLR) study that addressed two research questions about the mechanism for handling CWS faults, as well as fault prediction methods. This research rectified the associated research gaps found in the SLR study, which were related to three points; namely fault handling, fault frequencies, and fault solutions. A framework was built based on the outcome of an industry survey study and contained three parts: setup, machine learning, and quality control. The first part explained the three arrangements required for preparing the framework. The second part proposed a decision tree (DT) model to predict CWS faults and listed the steps for building and training the model. In this part, two DT algorithms were proposed, C4.5 and CART. The last part, quality control, suggested managerial steps for controlling the maintenance program. The framework was implemented in a university, with encouraging outcomes, as the prediction accuracy of the presented prediction model was more than 98% for each CWS component. The DT model improved the fault prediction by more than 20% in all CWS components when compared to the existing control system at the university. |
| first_indexed | 2024-03-11T09:03:02Z |
| format | Article |
| id | doaj.art-6d82efbe0f6540af86fb50d78eae9b2e |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-11T09:03:02Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-6d82efbe0f6540af86fb50d78eae9b2e2023-11-16T19:33:32ZengMDPI AGBuildings2075-53092023-02-0113249710.3390/buildings13020497Predictive Maintenance 4.0 for Chilled Water System at Commercial Buildings: A Methodological FrameworkMalek Almobarek0Kepa Mendibil1Abdalla Alrashdan2Department of Design, Manufacturing and Engineering Management, Faculty of Engineering, University of Strathclyde, Glasgow G1 1XQ, UKDepartment of Design, Manufacturing and Engineering Management, Faculty of Engineering, University of Strathclyde, Glasgow G1 1XQ, UKIndustrial Engineering Department, College of Engineering, Alfaisal University, Riyadh 50927, Saudi ArabiaPredictive maintenance is considered as one of the most important strategies for managing the utility systems of commercial buildings. This research focused on chilled water system (CWS) components and proposed a methodological framework to build a comprehensive predictive maintenance program in line with Industry 4.0/Quality 4.0 (PdM 4.0). This research followed a systematic literature review (SLR) study that addressed two research questions about the mechanism for handling CWS faults, as well as fault prediction methods. This research rectified the associated research gaps found in the SLR study, which were related to three points; namely fault handling, fault frequencies, and fault solutions. A framework was built based on the outcome of an industry survey study and contained three parts: setup, machine learning, and quality control. The first part explained the three arrangements required for preparing the framework. The second part proposed a decision tree (DT) model to predict CWS faults and listed the steps for building and training the model. In this part, two DT algorithms were proposed, C4.5 and CART. The last part, quality control, suggested managerial steps for controlling the maintenance program. The framework was implemented in a university, with encouraging outcomes, as the prediction accuracy of the presented prediction model was more than 98% for each CWS component. The DT model improved the fault prediction by more than 20% in all CWS components when compared to the existing control system at the university.https://www.mdpi.com/2075-5309/13/2/497predictive maintenanceIndustry 4.0Quality 4.0decision tree algorithmchilled water systemHVAC |
| spellingShingle | Malek Almobarek Kepa Mendibil Abdalla Alrashdan Predictive Maintenance 4.0 for Chilled Water System at Commercial Buildings: A Methodological Framework Buildings predictive maintenance Industry 4.0 Quality 4.0 decision tree algorithm chilled water system HVAC |
| title | Predictive Maintenance 4.0 for Chilled Water System at Commercial Buildings: A Methodological Framework |
| title_full | Predictive Maintenance 4.0 for Chilled Water System at Commercial Buildings: A Methodological Framework |
| title_fullStr | Predictive Maintenance 4.0 for Chilled Water System at Commercial Buildings: A Methodological Framework |
| title_full_unstemmed | Predictive Maintenance 4.0 for Chilled Water System at Commercial Buildings: A Methodological Framework |
| title_short | Predictive Maintenance 4.0 for Chilled Water System at Commercial Buildings: A Methodological Framework |
| title_sort | predictive maintenance 4 0 for chilled water system at commercial buildings a methodological framework |
| topic | predictive maintenance Industry 4.0 Quality 4.0 decision tree algorithm chilled water system HVAC |
| url | https://www.mdpi.com/2075-5309/13/2/497 |
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