文章基本信息

标题：DyD 2: Dynamic Double anomaly Detection Application to on-board space radiation faults
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作者：Adrien Dorise ; Louise Travé-Massuyès ; Audine Subias 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2022
卷号：55
期号：6
页码：205-210
DOI：10.1016/j.ifacol.2022.07.130
语种：English
出版社：Elsevier
摘要：AbstractAnomaly detection is a crucial aspect of embedded applications. However, limited computational power, evolving environments, and lack of training data are difficulties that can limit anomaly detection algorithms. One class classification algorithms are often used for this task to circumvent the need of anomalous data in the training set. This paper presents a new machine learning algorithm for anomaly detection called Dynamic Double anomaly Detection DyD2that is suited to evolving environments and on-board requirements. The contributions made by DyD2are thoroughly presented and an experimental evaluation is set up to compare DyD2to state-of-the-art algorithms.
关键词：KeywordsAnomaly detectionMachine learningOne-class classificationEmbedded applicationsAerospace engineeringSpace Radiations