Review of predictive maintenance algorithms applied to HVAC systems
Predictive maintenance is a preventive maintenance approach that is performed based on an online health assessment and allows for timely pre-failure interventions. It can diminish the cost of maintenance by reducing the frequency of maintenance as much as possible to avoid unplanned reactive mainten...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722013944 |
| _version_ | 1828010083774103552 |
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| author | Niima Es-sakali Moha Cherkaoui Mohamed Oualid Mghazli Zakaria Naimi |
| author_facet | Niima Es-sakali Moha Cherkaoui Mohamed Oualid Mghazli Zakaria Naimi |
| author_sort | Niima Es-sakali |
| collection | DOAJ |
| description | Predictive maintenance is a preventive maintenance approach that is performed based on an online health assessment and allows for timely pre-failure interventions. It can diminish the cost of maintenance by reducing the frequency of maintenance as much as possible to avoid unplanned reactive maintenance, without incurring the costs associated with too frequent preventive maintenance. The main objective of predictive maintenance of heating, ventilation, and air conditioning (HVAC) systems is to predict when the HVAC equipment failure may occur. The benefits are numerous: planning of maintenance before the failure occurs, reduction of maintenance costs, and increased reliability. For this, the predictive maintenance of the HVAC systems is based on the historical data of the system for predicting the state of health of the system. The process of predictive maintenance application is composed of the Internet of Things (IoT) sensors that are installed inside the HVAC system, then the IoT platforms that help in collecting the signals coming from the sensors and converting them to existing databases. Afterward, the algorithms of application of predictive maintenance could be either knowledge-based approaches, physics-based approaches, or even data-driven-based approaches. A systematic literature review on the existing algorithms of HVAC predictive maintenance application is conducted in this paper to summarize the most used approach for predicting future failures in HVAC systems and to explain the benefits and limits of these algorithms. |
| first_indexed | 2024-04-10T08:49:44Z |
| format | Article |
| id | doaj.art-01f3cba9e7534c219bc910fc563b3080 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-10T08:49:44Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-01f3cba9e7534c219bc910fc563b30802023-02-22T04:30:58ZengElsevierEnergy Reports2352-48472022-11-01810031012Review of predictive maintenance algorithms applied to HVAC systemsNiima Es-sakali0Moha Cherkaoui1Mohamed Oualid Mghazli2Zakaria Naimi3LMAID laboratory, Rabat National School of Mine, Mohamed-V University, 10070 Rabat, Morocco; Green Energy Park (IRESEN, UM6P), 43150 Benguerir, Morocco; Corresponding author at: LMAID laboratory, Rabat National School of Mine, Mohamed-V University, 10070 Rabat, Morocco.LMAID laboratory, Rabat National School of Mine, Mohamed-V University, 10070 Rabat, MoroccoGreen Energy Park (IRESEN, UM6P), 43150 Benguerir, Morocco; ENTPE, LTDS UMR CNRS 5513, Univ Lyon, Vaulx-en-Velin Cedex, FranceGreen Energy Park (IRESEN, UM6P), 43150 Benguerir, MoroccoPredictive maintenance is a preventive maintenance approach that is performed based on an online health assessment and allows for timely pre-failure interventions. It can diminish the cost of maintenance by reducing the frequency of maintenance as much as possible to avoid unplanned reactive maintenance, without incurring the costs associated with too frequent preventive maintenance. The main objective of predictive maintenance of heating, ventilation, and air conditioning (HVAC) systems is to predict when the HVAC equipment failure may occur. The benefits are numerous: planning of maintenance before the failure occurs, reduction of maintenance costs, and increased reliability. For this, the predictive maintenance of the HVAC systems is based on the historical data of the system for predicting the state of health of the system. The process of predictive maintenance application is composed of the Internet of Things (IoT) sensors that are installed inside the HVAC system, then the IoT platforms that help in collecting the signals coming from the sensors and converting them to existing databases. Afterward, the algorithms of application of predictive maintenance could be either knowledge-based approaches, physics-based approaches, or even data-driven-based approaches. A systematic literature review on the existing algorithms of HVAC predictive maintenance application is conducted in this paper to summarize the most used approach for predicting future failures in HVAC systems and to explain the benefits and limits of these algorithms.http://www.sciencedirect.com/science/article/pii/S2352484722013944Predictive maintenanceMachine learningDeep learningHVAC systems |
| spellingShingle | Niima Es-sakali Moha Cherkaoui Mohamed Oualid Mghazli Zakaria Naimi Review of predictive maintenance algorithms applied to HVAC systems Energy Reports Predictive maintenance Machine learning Deep learning HVAC systems |
| title | Review of predictive maintenance algorithms applied to HVAC systems |
| title_full | Review of predictive maintenance algorithms applied to HVAC systems |
| title_fullStr | Review of predictive maintenance algorithms applied to HVAC systems |
| title_full_unstemmed | Review of predictive maintenance algorithms applied to HVAC systems |
| title_short | Review of predictive maintenance algorithms applied to HVAC systems |
| title_sort | review of predictive maintenance algorithms applied to hvac systems |
| topic | Predictive maintenance Machine learning Deep learning HVAC systems |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722013944 |
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