Online dynamic working-state recognition through uncertain data classification

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dc.contributor.authorYan, Xiaozhen
dc.contributor.authorLuo, Qinghua
dc.contributor.authorSun, Jianyu
dc.contributor.authorLuo, Zhenhua
dc.contributor.authorChen, Yunsai
dc.date.accessioned2021-04-20T11:34:08Z
dc.date.available2021-04-20T11:34:08Z
dc.date.issued2020-11-28
dc.description.abstractThe satellite must continue working properly under different working environments and working loads. The power system is an essential component. Due to different working tasks, loads, and attitudes, a power system has many diverse working states. Therefore, it is necessary to accurately recognize the working state online for fault diagnostics and health management. However, under different working loads, measurement errors, environmental noise, environmental interference, and other uncertain factors, the output voltage value of a satellite power system has different levels of uncertainties. If these uncertainties and various working states are not considered, the recognition results can be of low quality. To address this problem and the uncertainty factors, we present an online dynamic working-state recognition system for satellite power systems based on uncertain data classification. In the system, we first explore the uncertain-data clustering center to model the working state. Then, with a slide-window processing strategy, we compute the distances between the uncertain cluster centers and the uncertain voltage data for the satellite power system online. Thus, we can obtain more accurate dynamic working-state recognition results. The evaluation results of real data demonstrate that the presented system is valid for working-state recognition and can be applied to a satellite power system.en_UK
dc.identifier.citationYan X, Luo Q, Sun J, et al., (2021) Online dynamic working-state recognition through uncertain data classification. Information Sciences, Volume 555, May 2021, pp. 1-16en_UK
dc.identifier.issn0020-0255
dc.identifier.urihttps://doi.org/10.1016/j.ins.2020.11.022
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/16597
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectSatellite poweren_UK
dc.subjectWorking-state recognitionen_UK
dc.subjectUncertainty dataen_UK
dc.subjectClassificationen_UK
dc.titleOnline dynamic working-state recognition through uncertain data classificationen_UK
dc.typeArticleen_UK

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