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Ponder: Enabling Balloon-Borne Based Solar Unmanned Aerial Vehicle’s Take Off Diagnosis under Little Data
Balloon-borne based solar unmanned aerial vehicle (short for BS-UAV) has been researched prevalently due to the promising application area of near-space (i.e., 20–100 km above the ground) and the advantages of taking off. However, BS-UAV encounters serious fault in its taking off phase. The fault in...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9318177/ https://www.ncbi.nlm.nih.gov/pubmed/35885220 http://dx.doi.org/10.3390/e24070997 |
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author | Hu, Yanfei Jiao, Yingkui Shang, Yujie Li, Shuailou Hu, Yanpeng |
author_facet | Hu, Yanfei Jiao, Yingkui Shang, Yujie Li, Shuailou Hu, Yanpeng |
author_sort | Hu, Yanfei |
collection | PubMed |
description | Balloon-borne based solar unmanned aerial vehicle (short for BS-UAV) has been researched prevalently due to the promising application area of near-space (i.e., 20–100 km above the ground) and the advantages of taking off. However, BS-UAV encounters serious fault in its taking off phase. The fault in taking off hinders the development of BS-UAV and causes great loss to human property. Thus, timely diagnosing the running state of BS-UAV in taking off phase is of great importance. Unfortunately, due to lack of fault data in the taking off phase, timely diagnosing the running state becomes a key challenge. In this paper, we propose Ponder to diagnose the running state of BS-UAV in the taking off phase. The key idea of Ponder is to take full advantage of existing data and complement fault data first and then diagnose current states. First, we compress existing data into a low-dimensional space. Then, we cluster the low-dimensional data into normal and outlier clusters. Third, we generate fault data with different aggression at different clusters. Finally, we diagnose fault state for each sampling at the taking off phase. With three datasets collected on real-world flying at different times, we show that Ponder outperforms existing diagnosing methods. In addition, we demonstrate Ponder’s effectiveness over time. We also show the comparable overhead. |
format | Online Article Text |
id | pubmed-9318177 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93181772022-07-27 Ponder: Enabling Balloon-Borne Based Solar Unmanned Aerial Vehicle’s Take Off Diagnosis under Little Data Hu, Yanfei Jiao, Yingkui Shang, Yujie Li, Shuailou Hu, Yanpeng Entropy (Basel) Article Balloon-borne based solar unmanned aerial vehicle (short for BS-UAV) has been researched prevalently due to the promising application area of near-space (i.e., 20–100 km above the ground) and the advantages of taking off. However, BS-UAV encounters serious fault in its taking off phase. The fault in taking off hinders the development of BS-UAV and causes great loss to human property. Thus, timely diagnosing the running state of BS-UAV in taking off phase is of great importance. Unfortunately, due to lack of fault data in the taking off phase, timely diagnosing the running state becomes a key challenge. In this paper, we propose Ponder to diagnose the running state of BS-UAV in the taking off phase. The key idea of Ponder is to take full advantage of existing data and complement fault data first and then diagnose current states. First, we compress existing data into a low-dimensional space. Then, we cluster the low-dimensional data into normal and outlier clusters. Third, we generate fault data with different aggression at different clusters. Finally, we diagnose fault state for each sampling at the taking off phase. With three datasets collected on real-world flying at different times, we show that Ponder outperforms existing diagnosing methods. In addition, we demonstrate Ponder’s effectiveness over time. We also show the comparable overhead. MDPI 2022-07-19 /pmc/articles/PMC9318177/ /pubmed/35885220 http://dx.doi.org/10.3390/e24070997 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Hu, Yanfei Jiao, Yingkui Shang, Yujie Li, Shuailou Hu, Yanpeng Ponder: Enabling Balloon-Borne Based Solar Unmanned Aerial Vehicle’s Take Off Diagnosis under Little Data |
title | Ponder: Enabling Balloon-Borne Based Solar Unmanned Aerial Vehicle’s Take Off Diagnosis under Little Data |
title_full | Ponder: Enabling Balloon-Borne Based Solar Unmanned Aerial Vehicle’s Take Off Diagnosis under Little Data |
title_fullStr | Ponder: Enabling Balloon-Borne Based Solar Unmanned Aerial Vehicle’s Take Off Diagnosis under Little Data |
title_full_unstemmed | Ponder: Enabling Balloon-Borne Based Solar Unmanned Aerial Vehicle’s Take Off Diagnosis under Little Data |
title_short | Ponder: Enabling Balloon-Borne Based Solar Unmanned Aerial Vehicle’s Take Off Diagnosis under Little Data |
title_sort | ponder: enabling balloon-borne based solar unmanned aerial vehicle’s take off diagnosis under little data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9318177/ https://www.ncbi.nlm.nih.gov/pubmed/35885220 http://dx.doi.org/10.3390/e24070997 |
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