Multivariate Features Extraction and Effective Decision Making Using Machine Learning Approaches
<p dir="ltr">Fault Detection and Isolation (FDI) in Heating, Ventilation, and Air Conditioning (HVAC) systems is an important approach to guarantee the human safety of these systems. Therefore, the implementation of a FDI framework is required to reduce the energy needs for buildings...
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2020
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| _version_ | 1864513511344832512 |
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| author | Sondes Gharsellaoui (16870047) |
| author2 | Majdi Mansouri (16869885) Shady S. Refaat (16864269) Haitham Abu-Rub (16855500) Hassani Messaoud (16870050) |
| author2_role | author author author author |
| author_facet | Sondes Gharsellaoui (16870047) Majdi Mansouri (16869885) Shady S. Refaat (16864269) Haitham Abu-Rub (16855500) Hassani Messaoud (16870050) |
| author_role | author |
| dc.creator.none.fl_str_mv | Sondes Gharsellaoui (16870047) Majdi Mansouri (16869885) Shady S. Refaat (16864269) Haitham Abu-Rub (16855500) Hassani Messaoud (16870050) |
| dc.date.none.fl_str_mv | 2020-01-31T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/en13030609 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Multivariate_Features_Extraction_and_Effective_Decision_Making_Using_Machine_Learning_Approaches/26015791 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Control engineering, mechatronics and robotics Electrical engineering Information and computing sciences Machine learning machine learning (ML) principal component analysis (PCA air conditioning systems feature extraction fault classification |
| dc.title.none.fl_str_mv | Multivariate Features Extraction and Effective Decision Making Using Machine Learning Approaches |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Fault Detection and Isolation (FDI) in Heating, Ventilation, and Air Conditioning (HVAC) systems is an important approach to guarantee the human safety of these systems. Therefore, the implementation of a FDI framework is required to reduce the energy needs for buildings and improving indoor environment quality. The main goal of this paper is to merge the benefits of multiscale representation, Principal Component Analysis (PCA), and Machine Learning (ML) classifiers to improve the efficiency of the detection and isolation of Air Conditioning (AC) systems. First, the multivariate statistical features extraction and selection is achieved using the PCA method. Then, the multiscale representation is applied to separate feature from noise and approximately decorrelate autocorrelation between available measurements. Third, the extracted and selected features are introduced to several machine learning classifiers for fault classification purposes. The effectiveness and higher classification accuracy of the developed Multiscale PCA (MSPCA)-based ML technique is demonstrated using two examples: synthetic data and simulated data extracted from Air Conditioning systems.</p><h2>Other Information</h2><p dir="ltr">Published in: Energies<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/en13030609" target="_blank">https://dx.doi.org/10.3390/en13030609</a></p><p dir="ltr">Additional institutions affiliated with: Electrical and Computer Engineering Program - TAMUQ</p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_428950e39e343e8e6e86221cdb84fe9b |
| identifier_str_mv | 10.3390/en13030609 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26015791 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Multivariate Features Extraction and Effective Decision Making Using Machine Learning ApproachesSondes Gharsellaoui (16870047)Majdi Mansouri (16869885)Shady S. Refaat (16864269)Haitham Abu-Rub (16855500)Hassani Messaoud (16870050)EngineeringControl engineering, mechatronics and roboticsElectrical engineeringInformation and computing sciencesMachine learningmachine learning (ML)principal component analysis (PCAair conditioning systemsfeature extractionfault classification<p dir="ltr">Fault Detection and Isolation (FDI) in Heating, Ventilation, and Air Conditioning (HVAC) systems is an important approach to guarantee the human safety of these systems. Therefore, the implementation of a FDI framework is required to reduce the energy needs for buildings and improving indoor environment quality. The main goal of this paper is to merge the benefits of multiscale representation, Principal Component Analysis (PCA), and Machine Learning (ML) classifiers to improve the efficiency of the detection and isolation of Air Conditioning (AC) systems. First, the multivariate statistical features extraction and selection is achieved using the PCA method. Then, the multiscale representation is applied to separate feature from noise and approximately decorrelate autocorrelation between available measurements. Third, the extracted and selected features are introduced to several machine learning classifiers for fault classification purposes. The effectiveness and higher classification accuracy of the developed Multiscale PCA (MSPCA)-based ML technique is demonstrated using two examples: synthetic data and simulated data extracted from Air Conditioning systems.</p><h2>Other Information</h2><p dir="ltr">Published in: Energies<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/en13030609" target="_blank">https://dx.doi.org/10.3390/en13030609</a></p><p dir="ltr">Additional institutions affiliated with: Electrical and Computer Engineering Program - TAMUQ</p>2020-01-31T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/en13030609https://figshare.com/articles/journal_contribution/Multivariate_Features_Extraction_and_Effective_Decision_Making_Using_Machine_Learning_Approaches/26015791CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/260157912020-01-31T03:00:00Z |
| spellingShingle | Multivariate Features Extraction and Effective Decision Making Using Machine Learning Approaches Sondes Gharsellaoui (16870047) Engineering Control engineering, mechatronics and robotics Electrical engineering Information and computing sciences Machine learning machine learning (ML) principal component analysis (PCA air conditioning systems feature extraction fault classification |
| status_str | publishedVersion |
| title | Multivariate Features Extraction and Effective Decision Making Using Machine Learning Approaches |
| title_full | Multivariate Features Extraction and Effective Decision Making Using Machine Learning Approaches |
| title_fullStr | Multivariate Features Extraction and Effective Decision Making Using Machine Learning Approaches |
| title_full_unstemmed | Multivariate Features Extraction and Effective Decision Making Using Machine Learning Approaches |
| title_short | Multivariate Features Extraction and Effective Decision Making Using Machine Learning Approaches |
| title_sort | Multivariate Features Extraction and Effective Decision Making Using Machine Learning Approaches |
| topic | Engineering Control engineering, mechatronics and robotics Electrical engineering Information and computing sciences Machine learning machine learning (ML) principal component analysis (PCA air conditioning systems feature extraction fault classification |