Interval-Valued Features Based Machine Learning Technique for Fault Detection and Diagnosis of Uncertain HVAC Systems
<p>The operation of heating, ventilation, and air conditioning (HVAC) systems is usually disturbed by many uncertainties such as measurement errors, noise, as well as temperature. Thus, this paper proposes a new multiscale interval principal component analysis (MSIPCA)-based machine learning (...
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2020
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| _version_ | 1864513562757562368 |
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| author | Sondes Gharsellaoui (16870047) |
| author2 | Majdi Mansouri (16869885) Mohamed Trabelsi (16869891) Mohamed-Faouzi Harkat (16869897) Shady S. Refaat (16864269) Hassani Messaoud (16870050) |
| author2_role | author author author author author |
| author_facet | Sondes Gharsellaoui (16870047) Majdi Mansouri (16869885) Mohamed Trabelsi (16869891) Mohamed-Faouzi Harkat (16869897) Shady S. Refaat (16864269) Hassani Messaoud (16870050) |
| author_role | author |
| dc.creator.none.fl_str_mv | Sondes Gharsellaoui (16870047) Majdi Mansouri (16869885) Mohamed Trabelsi (16869891) Mohamed-Faouzi Harkat (16869897) Shady S. Refaat (16864269) Hassani Messaoud (16870050) |
| dc.date.none.fl_str_mv | 2020-08-25T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2020.3019365 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Interval-Valued_Features_Based_Machine_Learning_Technique_for_Fault_Detection_and_Diagnosis_of_Uncertain_HVAC_Systems/24016068 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Mechanical engineering Information and computing sciences Machine learning Buildings Fault detection and diagnosis (FDD) Feature extraction Feature extraction and selection HVAC systems Interval-valued principal component analysis (IPCA) Machine learning (ML) Measurement errors Monitoring Model uncertainties Principal component analysis Standardization Uncertainty |
| dc.title.none.fl_str_mv | Interval-Valued Features Based Machine Learning Technique for Fault Detection and Diagnosis of Uncertain HVAC Systems |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>The operation of heating, ventilation, and air conditioning (HVAC) systems is usually disturbed by many uncertainties such as measurement errors, noise, as well as temperature. Thus, this paper proposes a new multiscale interval principal component analysis (MSIPCA)-based machine learning (ML) technique for fault detection and diagnosis (FDD) of uncertain HVAC systems. The main goal of the developed MSIPCA-ML approach is to enhance the diagnosis performance, improve the indoor environment quality, and minimize the energy consumption in uncertain building systems. The model uncertainty is addressed by considering the interval-valued data representation. The performance of the proposed FDD is investigated using sets of synthetic and emulated data extracted under different operating conditions. The presented results confirm the high-efficiency of the developed technique in monitoring uncertain HVAC systems due to the high diagnosis capabilities of the interval feature-based support vector machines and k-nearest neighbors and their ability to distinguish between the different operating modes of the HVAC system.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2020.3019365" target="_blank">https://dx.doi.org/10.1109/access.2020.3019365</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_81ac440fe409e5e81b268f6df2b1f2a4 |
| identifier_str_mv | 10.1109/access.2020.3019365 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24016068 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Interval-Valued Features Based Machine Learning Technique for Fault Detection and Diagnosis of Uncertain HVAC SystemsSondes Gharsellaoui (16870047)Majdi Mansouri (16869885)Mohamed Trabelsi (16869891)Mohamed-Faouzi Harkat (16869897)Shady S. Refaat (16864269)Hassani Messaoud (16870050)EngineeringElectrical engineeringMechanical engineeringInformation and computing sciencesMachine learningBuildingsFault detection and diagnosis (FDD)Feature extractionFeature extraction and selectionHVAC systemsInterval-valued principal component analysis (IPCA)Machine learning (ML)Measurement errorsMonitoringModel uncertaintiesPrincipal component analysisStandardizationUncertainty<p>The operation of heating, ventilation, and air conditioning (HVAC) systems is usually disturbed by many uncertainties such as measurement errors, noise, as well as temperature. Thus, this paper proposes a new multiscale interval principal component analysis (MSIPCA)-based machine learning (ML) technique for fault detection and diagnosis (FDD) of uncertain HVAC systems. The main goal of the developed MSIPCA-ML approach is to enhance the diagnosis performance, improve the indoor environment quality, and minimize the energy consumption in uncertain building systems. The model uncertainty is addressed by considering the interval-valued data representation. The performance of the proposed FDD is investigated using sets of synthetic and emulated data extracted under different operating conditions. The presented results confirm the high-efficiency of the developed technique in monitoring uncertain HVAC systems due to the high diagnosis capabilities of the interval feature-based support vector machines and k-nearest neighbors and their ability to distinguish between the different operating modes of the HVAC system.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2020.3019365" target="_blank">https://dx.doi.org/10.1109/access.2020.3019365</a></p>2020-08-25T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2020.3019365https://figshare.com/articles/journal_contribution/Interval-Valued_Features_Based_Machine_Learning_Technique_for_Fault_Detection_and_Diagnosis_of_Uncertain_HVAC_Systems/24016068CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240160682020-08-25T00:00:00Z |
| spellingShingle | Interval-Valued Features Based Machine Learning Technique for Fault Detection and Diagnosis of Uncertain HVAC Systems Sondes Gharsellaoui (16870047) Engineering Electrical engineering Mechanical engineering Information and computing sciences Machine learning Buildings Fault detection and diagnosis (FDD) Feature extraction Feature extraction and selection HVAC systems Interval-valued principal component analysis (IPCA) Machine learning (ML) Measurement errors Monitoring Model uncertainties Principal component analysis Standardization Uncertainty |
| status_str | publishedVersion |
| title | Interval-Valued Features Based Machine Learning Technique for Fault Detection and Diagnosis of Uncertain HVAC Systems |
| title_full | Interval-Valued Features Based Machine Learning Technique for Fault Detection and Diagnosis of Uncertain HVAC Systems |
| title_fullStr | Interval-Valued Features Based Machine Learning Technique for Fault Detection and Diagnosis of Uncertain HVAC Systems |
| title_full_unstemmed | Interval-Valued Features Based Machine Learning Technique for Fault Detection and Diagnosis of Uncertain HVAC Systems |
| title_short | Interval-Valued Features Based Machine Learning Technique for Fault Detection and Diagnosis of Uncertain HVAC Systems |
| title_sort | Interval-Valued Features Based Machine Learning Technique for Fault Detection and Diagnosis of Uncertain HVAC Systems |
| topic | Engineering Electrical engineering Mechanical engineering Information and computing sciences Machine learning Buildings Fault detection and diagnosis (FDD) Feature extraction Feature extraction and selection HVAC systems Interval-valued principal component analysis (IPCA) Machine learning (ML) Measurement errors Monitoring Model uncertainties Principal component analysis Standardization Uncertainty |