Data-Driven Designs of Fault Identification via Collaborative Deep Learning for Traction Systems in High-Speed Trains
Rattachement africain : cn, ca. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Due to the advanced development of sensor technology, the data deluge has begun in the complex systems of high-speed trains (HSTs) and, therefore, hastens the popularity of data-driven research. Among these activities, data-driven detection and identification of faults have received considerable attention to ensure the safe and reliable operations of HST, especially the deep learning-based methods. Up to now, these deep learning-based methods are effective only for static systems. It, hence, motivates us to develop the data-driven fault identification (FI) method for traction systems in HST. In this study, we will develop an FI method via the collaborative deep learning method, where the first neural network is used for eliminating dynamic behaviors, and the second neural network is responsible for identifying the fault amplitude. By the use of the proposed neural networks with a deep architecture, the FI task can be achieved in a collaborative fashion. Its successful application on the traction systems of HST illustrates the effectiveness of collaborative deep learning on the one hand and opens an avenue on the data-driven FI methods using neural networks on the other hand.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Data-Driven Designs of Fault Identification via Collaborative Deep Learning for Traction Systems in High-Speed Trains
- Date Crossref
- 01/06/2022
- Éditeur
- Institute of Electrical and Electronics Engineers (IEEE)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
-
Changchun University of Technology pays non établi dans la noticeUniversité ou école supérieure
-
Harbin Institute of Technology Department of Control Science and Engineering pays non établi dans la noticeUniversité ou école supérieure
-
University of Alberta Department of Chemical and Materials Engineering pays non établi dans la noticeUniversité ou école supérieure
-
School of Computer Science and Engineering pays non établi dans la noticeUniversité ou école supérieure
Changchun University of Technology, Department of Control Science and Engineering — Harbin Institute of Technology et Department of Chemical and Materials Engineering — University of Alberta, avec 1 autre affiliation.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.