A Novel Fault Diagnosis of Uncertain Systems Based on Interval Gaussian Process Regression: Application to Wind Energy Conversion Systems
<p>Fault detection and diagnosis (FDD) of wind energy conversion (WEC) systems play an important role in reducing the maintenance and operational costs and increase system reliability. Thus, this paper proposes a novel Interval Gaussian Process Regression (IGPR)-based Random Forest (RF) techni...
محفوظ في:
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| مؤلفون آخرون: | , , , , , , |
| منشور في: |
2020
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| _version_ | 1864513562752319488 |
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| author | Majdi Mansouri (16869885) |
| author2 | Radhia Fezai (16869888) Mohamed Trabelsi (16869891) Mansour Hajji (16869894) Mohamed-Faouzi Harkat (16869897) Hazem Nounou (16869900) Mohamed N. Nounou (16869903) Kais Bouzrara (16869906) |
| author2_role | author author author author author author author |
| author_facet | Majdi Mansouri (16869885) Radhia Fezai (16869888) Mohamed Trabelsi (16869891) Mansour Hajji (16869894) Mohamed-Faouzi Harkat (16869897) Hazem Nounou (16869900) Mohamed N. Nounou (16869903) Kais Bouzrara (16869906) |
| author_role | author |
| dc.creator.none.fl_str_mv | Majdi Mansouri (16869885) Radhia Fezai (16869888) Mohamed Trabelsi (16869891) Mansour Hajji (16869894) Mohamed-Faouzi Harkat (16869897) Hazem Nounou (16869900) Mohamed N. Nounou (16869903) Kais Bouzrara (16869906) |
| dc.date.none.fl_str_mv | 2020-12-02T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2020.3042101 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_Novel_Fault_Diagnosis_of_Uncertain_Systems_Based_on_Interval_Gaussian_Process_Regression_Application_to_Wind_Energy_Conversion_Systems/24015573 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Environmental engineering Data models Data mining Feature extraction Feature extraction and selection Fault detection and diagnosis (FDD) Gaussian processes Gaussian process regression (GPR) Interval-valued data Mathematical model Radio frequency Random forest (RF) Wind turbines Wind energy conversion (WEC) systems |
| dc.title.none.fl_str_mv | A Novel Fault Diagnosis of Uncertain Systems Based on Interval Gaussian Process Regression: Application to Wind Energy Conversion Systems |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Fault detection and diagnosis (FDD) of wind energy conversion (WEC) systems play an important role in reducing the maintenance and operational costs and increase system reliability. Thus, this paper proposes a novel Interval Gaussian Process Regression (IGPR)-based Random Forest (RF) technique (IGPR-RF) for diagnosing uncertain WEC systems. In the proposed IGPR-RF technique, the effective interval-valued nonlinear statistical features are extracted and selected using the IGPR model and then fed to the RF algorithm for fault classification purposes. The proposed technique is characterized by a better handling of WEC system uncertainties such as wind variability, noise, measurement errors, which leads to an improved fault classification accuracy. The obtained results show that the proposed IGPR-RF technique is characterized by a high diagnosis accuracy (an average accuracy of 99.99%) compared to the conventional classifiers.</p> <h3>Other Information</h3> <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.2020.3042101" target="_blank">https://dx.doi.org/10.1109/access.2020.3042101</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_9bf83bdfaaeb5ae81ec4059815b9a86b |
| identifier_str_mv | 10.1109/access.2020.3042101 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24015573 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A Novel Fault Diagnosis of Uncertain Systems Based on Interval Gaussian Process Regression: Application to Wind Energy Conversion SystemsMajdi Mansouri (16869885)Radhia Fezai (16869888)Mohamed Trabelsi (16869891)Mansour Hajji (16869894)Mohamed-Faouzi Harkat (16869897)Hazem Nounou (16869900)Mohamed N. Nounou (16869903)Kais Bouzrara (16869906)EngineeringElectrical engineeringEnvironmental engineeringData modelsData miningFeature extractionFeature extraction and selectionFault detection and diagnosis (FDD)Gaussian processesGaussian process regression (GPR)Interval-valued dataMathematical modelRadio frequencyRandom forest (RF)Wind turbinesWind energy conversion (WEC) systems<p>Fault detection and diagnosis (FDD) of wind energy conversion (WEC) systems play an important role in reducing the maintenance and operational costs and increase system reliability. Thus, this paper proposes a novel Interval Gaussian Process Regression (IGPR)-based Random Forest (RF) technique (IGPR-RF) for diagnosing uncertain WEC systems. In the proposed IGPR-RF technique, the effective interval-valued nonlinear statistical features are extracted and selected using the IGPR model and then fed to the RF algorithm for fault classification purposes. The proposed technique is characterized by a better handling of WEC system uncertainties such as wind variability, noise, measurement errors, which leads to an improved fault classification accuracy. The obtained results show that the proposed IGPR-RF technique is characterized by a high diagnosis accuracy (an average accuracy of 99.99%) compared to the conventional classifiers.</p> <h3>Other Information</h3> <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.2020.3042101" target="_blank">https://dx.doi.org/10.1109/access.2020.3042101</a></p>2020-12-02T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2020.3042101https://figshare.com/articles/journal_contribution/A_Novel_Fault_Diagnosis_of_Uncertain_Systems_Based_on_Interval_Gaussian_Process_Regression_Application_to_Wind_Energy_Conversion_Systems/24015573CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240155732020-12-02T00:00:00Z |
| spellingShingle | A Novel Fault Diagnosis of Uncertain Systems Based on Interval Gaussian Process Regression: Application to Wind Energy Conversion Systems Majdi Mansouri (16869885) Engineering Electrical engineering Environmental engineering Data models Data mining Feature extraction Feature extraction and selection Fault detection and diagnosis (FDD) Gaussian processes Gaussian process regression (GPR) Interval-valued data Mathematical model Radio frequency Random forest (RF) Wind turbines Wind energy conversion (WEC) systems |
| status_str | publishedVersion |
| title | A Novel Fault Diagnosis of Uncertain Systems Based on Interval Gaussian Process Regression: Application to Wind Energy Conversion Systems |
| title_full | A Novel Fault Diagnosis of Uncertain Systems Based on Interval Gaussian Process Regression: Application to Wind Energy Conversion Systems |
| title_fullStr | A Novel Fault Diagnosis of Uncertain Systems Based on Interval Gaussian Process Regression: Application to Wind Energy Conversion Systems |
| title_full_unstemmed | A Novel Fault Diagnosis of Uncertain Systems Based on Interval Gaussian Process Regression: Application to Wind Energy Conversion Systems |
| title_short | A Novel Fault Diagnosis of Uncertain Systems Based on Interval Gaussian Process Regression: Application to Wind Energy Conversion Systems |
| title_sort | A Novel Fault Diagnosis of Uncertain Systems Based on Interval Gaussian Process Regression: Application to Wind Energy Conversion Systems |
| topic | Engineering Electrical engineering Environmental engineering Data models Data mining Feature extraction Feature extraction and selection Fault detection and diagnosis (FDD) Gaussian processes Gaussian process regression (GPR) Interval-valued data Mathematical model Radio frequency Random forest (RF) Wind turbines Wind energy conversion (WEC) systems |