Application of Knowledge Graph Technology with Integrated Feature Data in Spacecraft Anomaly Detection

Author:

Yi Xiaojian123ORCID,Huang Peizheng12,Che Shangjie1

Affiliation:

1. School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China

2. Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing 314003, China

3. Tangshan Research Institute, Beijing Institute of Technology, Tangshan 063099, China

Abstract

Given the complexity of spacecraft system structures and functions, existing data-driven methods for anomaly detection face issues of insufficient interpretability and excessive dependence on historical data. To address these challenging problems, this paper proposes a method for applying knowledge graph technology with integrated feature data in spacecraft anomaly detection. First, the ontology concepts of the spacecraft equipment knowledge graph are designed according to expert knowledge, and then feature data are extracted from the historical operation data of the spacecraft in various states to build a rich spacecraft equipment knowledge graph. Next, spacecraft anomaly event knowledge graphs are constructed based on various types of anomaly features. During spacecraft operation, telemetry data are matched with the feature data in the knowledge graph, enabling anomaly device location and anomaly cause judgment. Experimental results show that this method, which utilizes spacecraft anomaly prior knowledge for anomaly detection and causes interpretation, has high practicality and efficiency. This research demonstrates the promising application prospects of knowledge graph technology in the field of spacecraft anomaly detection.

Funder

Equipment Development Department

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Reference46 articles.

1. Fault Detection for in-orbit Satellites Using an Adaptive Prediction Model;Li;Chin. J. Space Sci.,2014

2. Method of satellite anomaly detection based on least squares support vector machine;Chen;Comput. Meas. Control,2014

3. Yairi, T., Kawahara, Y., Fujimaki, R., Sato, Y., and Machida, K. (2006, January 17–20). Telemetry-mining: A machine learning approach to anomaly detection and fault diagnosis for space systems. Proceedings of the 2006 2nd IEEE International Conference on Space Mission Challenges for Information Technology, Pasadena, CA, USA.

4. Chen, J. (2015). Similarity Measure of Time Series for Satellite Telemetry Data. [Master’s Thesis, Harbin Institute of Technology].

5. Yairi, T., Ogasawara, S., Hori, K., Nakasuka, S., and Ishihama, N. (2004, January 26–28). Summarization of Spacecraft Telemetry Data by Extracting Significant Temporal Patterns. Proceedings of the 8th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD 2004), Sydney, Australia.

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3