DC serial arc fault recognition in aircraft using machine learning techniques
Arc fault detection represents one of the major challenges for protection systems used in aeronautical industry due to the high demand in terms of reliability, robustness and detection time. Current aircraft has no system capable of recognize arc faults respecting those requirements. The problem bec...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | International Journal of Electrical Power & Energy Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061524006318 |
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| author | Raul Carreira Rufato Thierry Ditchi Cyril Van de Steen Thierry Lebey Yacine Oussar |
| author_facet | Raul Carreira Rufato Thierry Ditchi Cyril Van de Steen Thierry Lebey Yacine Oussar |
| author_sort | Raul Carreira Rufato |
| collection | DOAJ |
| description | Arc fault detection represents one of the major challenges for protection systems used in aeronautical industry due to the high demand in terms of reliability, robustness and detection time. Current aircraft has no system capable of recognize arc faults respecting those requirements. The problem becomes harder with the voltage levels increase expected in More Electric Aircraft (MEA) and for all electric or hybrid propulsion, pushing studies to consider more carefully the phenomenon of arc faults. This study proposes a machine learning approach to help detect DC serial arcs, which is a challenge in terms of recognition. The analysed database contains both current signal measurements of arc faults and nominal behaviours. A classifier is implemented based on the extraction of relevant features from the conventional current signals. The selection and design of the model is based on the Internal SVM – RFE technique. The obtained results demonstrate the best trade-off between all the performance requirements. The methodology is able to achieve a recognition rate of 98%. |
| format | Article |
| id | doaj-art-56dd68056e7846749015215027db98b0 |
| institution | Kabale University |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-56dd68056e7846749015215027db98b02025-01-19T06:23:56ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-03-01164110408DC serial arc fault recognition in aircraft using machine learning techniquesRaul Carreira Rufato0Thierry Ditchi1Cyril Van de Steen2Thierry Lebey3Yacine Oussar4Safran Tech, Safran Group, 31700 Blagnac, France; LPEM, ESPCI Paris - PSL, CNRS, Sorbonne Université, 75005 Paris, France; Corresponding author at: 1 Rue Louis Blériot, 31702 Blagnac, France.LPEM, ESPCI Paris - PSL, CNRS, Sorbonne Université, 75005 Paris, FranceSafran Tech, Safran Group, 31700 Blagnac, FranceSafran Tech, Safran Group, 31700 Blagnac, FranceLPEM, ESPCI Paris - PSL, CNRS, Sorbonne Université, 75005 Paris, FranceArc fault detection represents one of the major challenges for protection systems used in aeronautical industry due to the high demand in terms of reliability, robustness and detection time. Current aircraft has no system capable of recognize arc faults respecting those requirements. The problem becomes harder with the voltage levels increase expected in More Electric Aircraft (MEA) and for all electric or hybrid propulsion, pushing studies to consider more carefully the phenomenon of arc faults. This study proposes a machine learning approach to help detect DC serial arcs, which is a challenge in terms of recognition. The analysed database contains both current signal measurements of arc faults and nominal behaviours. A classifier is implemented based on the extraction of relevant features from the conventional current signals. The selection and design of the model is based on the Internal SVM – RFE technique. The obtained results demonstrate the best trade-off between all the performance requirements. The methodology is able to achieve a recognition rate of 98%.http://www.sciencedirect.com/science/article/pii/S0142061524006318Serial DC arc fault detection Machine learningInternal SVM – RFEReliabilityRobustnessDetection time |
| spellingShingle | Raul Carreira Rufato Thierry Ditchi Cyril Van de Steen Thierry Lebey Yacine Oussar DC serial arc fault recognition in aircraft using machine learning techniques International Journal of Electrical Power & Energy Systems Serial DC arc fault detection Machine learning Internal SVM – RFE Reliability Robustness Detection time |
| title | DC serial arc fault recognition in aircraft using machine learning techniques |
| title_full | DC serial arc fault recognition in aircraft using machine learning techniques |
| title_fullStr | DC serial arc fault recognition in aircraft using machine learning techniques |
| title_full_unstemmed | DC serial arc fault recognition in aircraft using machine learning techniques |
| title_short | DC serial arc fault recognition in aircraft using machine learning techniques |
| title_sort | dc serial arc fault recognition in aircraft using machine learning techniques |
| topic | Serial DC arc fault detection Machine learning Internal SVM – RFE Reliability Robustness Detection time |
| url | http://www.sciencedirect.com/science/article/pii/S0142061524006318 |
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