文章基本信息

标题：A comparison of data-driven fault detection methods with application to aerospace electro-mechanical actuators
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作者：M. Mazzoleni ; Y. Maccarana ; F. Previdi 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2017
卷号：50
期号：1
页码：12797-12802
DOI：10.1016/j.ifacol.2017.08.1837
语种：English
出版社：Elsevier
摘要：AbstractIn this paper, a model-free framework is proposed in order to equip electromechanical actuators, deployed in aerospace applications, with health-monitoring capabilities. A large experimental activity has been carried out to perform acquisitions with both healthy and faulty components, taking into consideration the standard regulations for environmental testing of avionics hardware. The injected faults followed a Fault Tree Analysis and Failure Mode and Effect Analysis. Features, belonging to different domains, have been extracted from the measured signals. These indexes are based largely on the motor driving currents, in order to avoid the installation of new sensors. Finally, a Gradient Tree Boosting algorithm has been chosen to detect the system status: the choice has been dictated by a comparison with other known classification algorithms. Furthermore, the most promising features for a classification point of view are reported.
关键词：KeywordsFault DetectionMachine LearningElectro-Mechanical Actuators