WEKO3
アイテム
Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels
http://hdl.handle.net/10228/0002000245
http://hdl.handle.net/10228/0002000245d1d2d871-1f94-49e9-9502-678d7fa106eb
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
LaSEINE-2022_010.pdf (2.1 MB)
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| アイテムタイプ | 学術雑誌論文 = Journal Article(1) | |||||||||||||||
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| 公開日 | 2023-11-10 | |||||||||||||||
| 資源タイプ | ||||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||||
| 資源タイプ | journal article | |||||||||||||||
| タイトル | ||||||||||||||||
| タイトル | Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels | |||||||||||||||
| 言語 | en | |||||||||||||||
| 言語 | ||||||||||||||||
| 言語 | eng | |||||||||||||||
| 著者 |
Jara Cespedes, Adolfo Javier
× Jara Cespedes, Adolfo Javier
× Bramandika Holy Bagas Pangestu
× 花沢, 明俊
WEKO
23300
× 趙, 孟佑 |
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| 抄録 | ||||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||||
| 内容記述 | CubeSat requirements in terms of size, weight, and power restrict the possibility of having redundant systems. Consequently, telemetry data are the primary way to verify the status of the satellites in operation. The monitoring and interpretation of telemetry parameters relies on the operator’s experience. Therefore, telemetry data analysis is less reliable, considering the data’s complexity. This paper presents a Machine Learning (ML) approach to detecting anomalies in solar panel systems. The main challenge inherited from CubeSat is its capability to perform onboard inference of the ML model. Nowadays, several simple yet powerful ML algorithms for performing anomaly detection are available. This study investigates five ML algorithm candidates, considering classification score, execution time, model size, and power consumption in a constrained computational environment. The pre-processing stage introduces the windowed averaging technique besides standardization and principal component analysis. Furthermore, the paper features the background, bus system, and initial operational data of BIRDS-4, a constellation made of three 1U CubeSats released from the International Space Station in March 2021, with a ML model proposal for future satellite missions. | |||||||||||||||
| 言語 | en | |||||||||||||||
| 書誌情報 |
en : Applied Sciences 巻 12, 号 17, p. 8634, 発行日 2022-08-29 |
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| 出版社 | ||||||||||||||||
| 出版者 | MDPI | |||||||||||||||
| DOI | ||||||||||||||||
| 関連タイプ | isIdenticalTo | |||||||||||||||
| 識別子タイプ | DOI | |||||||||||||||
| 関連識別子 | https://doi.org/10.3390/app12178634 | |||||||||||||||
| ISSN | ||||||||||||||||
| 収録物識別子タイプ | EISSN | |||||||||||||||
| 収録物識別子 | 2076-3417 | |||||||||||||||
| 著作権関連情報 | ||||||||||||||||
| 権利情報Resource | https:// creativecommons.org/licenses/by/ 4.0/ | |||||||||||||||
| 権利情報 | Copyright (c) 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CCBY) license. | |||||||||||||||
| キーワード | ||||||||||||||||
| 主題Scheme | Other | |||||||||||||||
| 主題 | anomaly detection | |||||||||||||||
| キーワード | ||||||||||||||||
| 主題Scheme | Other | |||||||||||||||
| 主題 | BIRDS project | |||||||||||||||
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| 主題Scheme | Other | |||||||||||||||
| 主題 | CubeSat | |||||||||||||||
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| 主題Scheme | Other | |||||||||||||||
| 主題 | machine learning | |||||||||||||||
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| 主題Scheme | Other | |||||||||||||||
| 主題 | electrical power system | |||||||||||||||
| 出版タイプ | ||||||||||||||||
| 出版タイプ | VoR | |||||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||||
| 査読の有無 | ||||||||||||||||
| 値 | yes | |||||||||||||||
