| DOI | Trouver le DOI : https://doi.org/10.1017/aer.2023.103 |
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| Auteur | Rechercher : Cheung, C.1Identifiant ORCID : https://orcid.org/0000-0002-0696-8405; Rechercher : Seabrook, E.1Identifiant ORCID : https://orcid.org/0009-0003-5754-0446; Rechercher : Valdés, J. J.2Identifiant ORCID : https://orcid.org/0000-0003-2930-0325; Rechercher : Hamaimou, Z. A.1Identifiant ORCID : https://orcid.org/0009-0000-8450-1651; Rechercher : Biondic, C.1Identifiant ORCID : https://orcid.org/0009-0003-8370-482X |
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| Affiliation | - Conseil national de recherches Canada. Aérospatiale
- Conseil national de recherches Canada. Technologies numériques
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| Format | Texte, Article |
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| Conférence | The 19th and 20th Australian International Aerospace Congresses |
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| Sujet | load estimation; health and usage monitoring; integrated vehicle health management; ensembles; machine learning |
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| Résumé | AbstractHelicopter component load estimation can be achieved through a variety of machine learning techniques and algorithms. A range of ensemble integration techniques were investigated in order to leverage multiple machine learning models to estimate main rotor yoke loads from flight state and control system parameters. The techniques included simple averaging, weighted averaging and forward selection. Performance of the models was evaluated using four metrics: root mean squared error, correlation coefficient and the interquartile ranges of these two metrics. When compared, every ensemble outperformed the best individual model. The ensembles using forward selection achieved the best performance. The resulting output is more robust, more highly correlated and achieves lower error values as compared to the top individual models. While individual model outputs can vary significantly, confidence in their results can be greatly increased through the use of a diverse set of models and ensemble techniques. |
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| Date de publication | 2023-11-03 |
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| Maison d’édition | Cambridge University Press |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Identificateur | S0001924023001033 |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | d5dd178c-51da-4f8c-b385-89c9644b8f15 |
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| Enregistrement créé | 2024-04-12 |
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| Enregistrement modifié | 2024-04-15 |
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