Fault diagnosis for space utilisation
The space application task is to carry out various scientific experiments and applied research by using the ability of space experiment of spacecraft. In the past 20 years, >50 space application studies have been carried out in Chinese manned space flight application system, >500 units have be...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-10-01
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| Series: | The Journal of Engineering |
| Subjects: | |
| Online Access: | https://digital-library.theiet.org/content/journals/10.1049/joe.2018.9102 |
| _version_ | 1819069258180591616 |
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| author | Yuanyuan Sun Lili Guo Yongming Wang Zhongsong Ma Yi Niu |
| author_facet | Yuanyuan Sun Lili Guo Yongming Wang Zhongsong Ma Yi Niu |
| author_sort | Yuanyuan Sun |
| collection | DOAJ |
| description | The space application task is to carry out various scientific experiments and applied research by using the ability of space experiment of spacecraft. In the past 20 years, >50 space application studies have been carried out in Chinese manned space flight application system, >500 units have been involved in the previous flight missions, and fruitful results have been achieved. The white paper ‘Chinese spaceflight in 2016’ pointed out that in the next 5 years, Chinese satellite system will enhance the level and basic ability to construct the satellite system. Chinese manned space station project is scheduled to be completed ∼2022 and it will plan to operate >10 years. The space station, based on the world-wide integrated information network, has a large number of payloads and will become a national space laboratory. Space activities are full of risks and challenges. On the basis of a great deal of literatures, the method of avoiding space risk in the field of spaceflight is discussed. Aiming at the fault diagnosis task for space utilisation, the intelligent methods of deep learning including deep belief network, convolutional neural network and generative adversarial network are discussed. |
| first_indexed | 2024-12-21T16:47:11Z |
| format | Article |
| id | doaj.art-991016a3e43749e88e79c33baad2ff7e |
| institution | Directory Open Access Journal |
| issn | 2051-3305 |
| language | English |
| last_indexed | 2024-12-21T16:47:11Z |
| publishDate | 2019-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Journal of Engineering |
| spelling | doaj.art-991016a3e43749e88e79c33baad2ff7e2022-12-21T18:56:57ZengWileyThe Journal of Engineering2051-33052019-10-0110.1049/joe.2018.9102JOE.2018.9102Fault diagnosis for space utilisationYuanyuan Sun0Lili Guo1Yongming Wang2Zhongsong Ma3Yi Niu4School of Cyber Security, University of Chinese Academy of SciencesTechnology and Engineering Center for Space UtilizationInstitution of Information EngineeringTechnology and Engineering Center for Space UtilizationTechnology and Engineering Center for Space UtilizationThe space application task is to carry out various scientific experiments and applied research by using the ability of space experiment of spacecraft. In the past 20 years, >50 space application studies have been carried out in Chinese manned space flight application system, >500 units have been involved in the previous flight missions, and fruitful results have been achieved. The white paper ‘Chinese spaceflight in 2016’ pointed out that in the next 5 years, Chinese satellite system will enhance the level and basic ability to construct the satellite system. Chinese manned space station project is scheduled to be completed ∼2022 and it will plan to operate >10 years. The space station, based on the world-wide integrated information network, has a large number of payloads and will become a national space laboratory. Space activities are full of risks and challenges. On the basis of a great deal of literatures, the method of avoiding space risk in the field of spaceflight is discussed. Aiming at the fault diagnosis task for space utilisation, the intelligent methods of deep learning including deep belief network, convolutional neural network and generative adversarial network are discussed.https://digital-library.theiet.org/content/journals/10.1049/joe.2018.9102aerospace industryspace vehiclesremote sensingbelief networksfault diagnosislearning (artificial intelligence)satellite communicationspace researchartificial satelliteswhite paper chinese spaceflightchinese satellite systemsatellite communicationssatellite navigationchinese manned space station projectnational space laboratoryspace activitiesspace riskfault diagnosis taskspace utilisationspace application taskscientific experimentschinese space industryspace applicationschinese manned space flight application systemintegrated information networkdeep learningdeep belief networkconvolutional neural networkgenerative adversarial network |
| spellingShingle | Yuanyuan Sun Lili Guo Yongming Wang Zhongsong Ma Yi Niu Fault diagnosis for space utilisation The Journal of Engineering aerospace industry space vehicles remote sensing belief networks fault diagnosis learning (artificial intelligence) satellite communication space research artificial satellites white paper chinese spaceflight chinese satellite system satellite communications satellite navigation chinese manned space station project national space laboratory space activities space risk fault diagnosis task space utilisation space application task scientific experiments chinese space industry space applications chinese manned space flight application system integrated information network deep learning deep belief network convolutional neural network generative adversarial network |
| title | Fault diagnosis for space utilisation |
| title_full | Fault diagnosis for space utilisation |
| title_fullStr | Fault diagnosis for space utilisation |
| title_full_unstemmed | Fault diagnosis for space utilisation |
| title_short | Fault diagnosis for space utilisation |
| title_sort | fault diagnosis for space utilisation |
| topic | aerospace industry space vehicles remote sensing belief networks fault diagnosis learning (artificial intelligence) satellite communication space research artificial satellites white paper chinese spaceflight chinese satellite system satellite communications satellite navigation chinese manned space station project national space laboratory space activities space risk fault diagnosis task space utilisation space application task scientific experiments chinese space industry space applications chinese manned space flight application system integrated information network deep learning deep belief network convolutional neural network generative adversarial network |
| url | https://digital-library.theiet.org/content/journals/10.1049/joe.2018.9102 |
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