{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T13:57:20Z","timestamp":1775138240434,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,3,22]],"date-time":"2021-03-22T00:00:00Z","timestamp":1616371200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Unmanned Aerial Vehicles are expected to create enormous benefits to society, but there are safety concerns in recognizing faults at the vehicle\u2019s control component. Prior studies proposed various fault detection approaches leveraging heuristics-based rules and supervised learning-based models, but there were several drawbacks. The rule-based approaches required an engineer to update the rules on every type of fault, and the supervised learning-based approaches necessitated the acquisition of a finely-labeled training dataset. Moreover, both prior approaches commonly include a limit that the detection model can identify the trained type of faults only, but fail to recognize the unseen type of faults. In pursuit of resolving the aforementioned drawbacks, we proposed a fault detection model utilizing a stacked autoencoder that lies under unsupervised learning. The autoencoder was trained with data from safe UAV states, and its reconstruction loss was examined to distinguish the safe states and faulty states. The key contributions of our study are, as follows. First, we presented a series of analyses to extract essential features from raw UAV flight logs. Second, we designed a fault detection model consisting of the stacked autoencoder and the classifier. Lastly, we validated our approach\u2019s fault detection performance with two datasets consisting of different types of UAV faults.<\/jats:p>","DOI":"10.3390\/s21062208","type":"journal-article","created":{"date-parts":[[2021,3,22]],"date-time":"2021-03-22T12:55:07Z","timestamp":1616417707000},"page":"2208","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Unsupervised Fault Detection on Unmanned Aerial Vehicles: Encoding and Thresholding Approach"],"prefix":"10.3390","volume":"21","author":[{"given":"Kyung Ho","family":"Park","sequence":"first","affiliation":[{"name":"Graduate School of Cybersecurity, Korea University, Seoul 02841, Korea"}]},{"given":"Eunji","family":"Park","sequence":"additional","affiliation":[{"name":"Graduate School of Cybersecurity, Korea University, Seoul 02841, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0760-8807","authenticated-orcid":false,"given":"Huy Kang","family":"Kim","sequence":"additional","affiliation":[{"name":"Graduate School of Cybersecurity, Korea University, Seoul 02841, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,22]]},"reference":[{"key":"ref_1","unstructured":"Puri, A. 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