, Nous proposons une méthode de détection d'anomalies dans les données comportementales, exploitant les motifs séquentiels préalablement extraits pour décrire le comportement normal d'un train. De plus, l'outil présenté offre aux experts les informations utiles pour procéder au diagnostic des anomalies détectées, et pour assister la prise de décisions de maintenance. Les travaux présentés ouvrent de nombreuses perspectives

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