Detecting structural anomalies of quadcopter UAVs based on LSTM autoencoder
Detecting a structural anomaly, such as a damaged propeller or motor, is crucial for mission-critical operation of unmanned aerial vehicles (UAVs). The existing solutions often fail to detect structural anomalies because the pre-defined parameters required for the solution are limited in reflecting...
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| Published in: | Pervasive and mobile computing Vol. 88; p. 101736 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.01.2023
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| ISSN: | 1574-1192, 1873-1589 |
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| Abstract | Detecting a structural anomaly, such as a damaged propeller or motor, is crucial for mission-critical operation of unmanned aerial vehicles (UAVs). The existing solutions often fail to detect structural anomalies because the pre-defined parameters required for the solution are limited in reflecting the flight pattern or the external environment, such as wind conditions. In this paper, we propose a method for detecting structural anomalies in quadcopter UAVs, using only regular data and specifically considering flight patterns and runtime flight conditions. To this end, we employ a long short-term memory (LSTM) autoencoder model to learn complex features of regular flight data. While flying the UAV, the trained model estimates the degree of outlierness of the incoming data and assesses abnormal behavior of UAV by adaptively considering its movement. This way, the proposed method accurately detects structural anomalies in UAVs regardless of the runtime environment or flight mission. Our experiment results with an off-the-shelf UAV show that the proposed approach detects diverse structural anomalies by an average of 98.6% specificity and 90.3% sensitivity. |
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| AbstractList | Detecting a structural anomaly, such as a damaged propeller or motor, is crucial for mission-critical operation of unmanned aerial vehicles (UAVs). The existing solutions often fail to detect structural anomalies because the pre-defined parameters required for the solution are limited in reflecting the flight pattern or the external environment, such as wind conditions. In this paper, we propose a method for detecting structural anomalies in quadcopter UAVs, using only regular data and specifically considering flight patterns and runtime flight conditions. To this end, we employ a long short-term memory (LSTM) autoencoder model to learn complex features of regular flight data. While flying the UAV, the trained model estimates the degree of outlierness of the incoming data and assesses abnormal behavior of UAV by adaptively considering its movement. This way, the proposed method accurately detects structural anomalies in UAVs regardless of the runtime environment or flight mission. Our experiment results with an off-the-shelf UAV show that the proposed approach detects diverse structural anomalies by an average of 98.6% specificity and 90.3% sensitivity. |
| ArticleNumber | 101736 |
| Author | Kim, Jiwon Kang, Jaeyun Jeon, Seunghyeok Cha, Hojung |
| Author_xml | – sequence: 1 givenname: Seunghyeok surname: Jeon fullname: Jeon, Seunghyeok – sequence: 2 givenname: Jaeyun surname: Kang fullname: Kang, Jaeyun – sequence: 3 givenname: Jiwon surname: Kim fullname: Kim, Jiwon – sequence: 4 givenname: Hojung surname: Cha fullname: Cha, Hojung email: hjcha@yonsei.ac.kr |
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| Cites_doi | 10.1016/j.neucom.2016.12.038 10.1109/JIOT.2017.2737479 10.1109/TMECH.2019.2947250 10.1162/neco.1997.9.8.1735 10.3390/robotics8030059 10.1111/j.1471-0528.2008.01700.x 10.1016/j.asoc.2019.105650 10.1136/bmj.308.6943.1552 10.1038/nature14542 10.1145/1541880.1541882 10.3390/s21248253 10.1007/978-3-030-63823-8_83 10.1145/3213526.3213531 10.1007/s10846-012-9757-7 10.1007/s10846-015-0284-1 |
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| Snippet | Detecting a structural anomaly, such as a damaged propeller or motor, is crucial for mission-critical operation of unmanned aerial vehicles (UAVs). The... |
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| StartPage | 101736 |
| SubjectTerms | LSTM autoencoder Structural anomaly detection UAV |
| Title | Detecting structural anomalies of quadcopter UAVs based on LSTM autoencoder |
| URI | https://dx.doi.org/10.1016/j.pmcj.2022.101736 |
| Volume | 88 |
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