Unsupervised fault detection on unmanned aerial vehicles: Encoding and thresholding approach

Kyung Ho Park, Eunji Park, Huy Kang Kim

Research output: Contribution to journalArticlepeer-review

Abstract

Unmanned Aerial Vehicles are expected to create enormous benefits to society, but there are safety concerns in recognizing faults at the vehicle’s 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’s fault detection performance with two datasets consisting of different types of UAV faults.

Original languageEnglish
Article number2208
Pages (from-to)1-17
Number of pages17
JournalSensors
Volume21
Issue number6
DOIs
Publication statusPublished - 2021 Mar 2

Keywords

  • Anomaly detection
  • Autoencoder
  • Fault detection
  • Unmanned Aerial Vehicle
  • Unsupervised learning

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Unsupervised fault detection on unmanned aerial vehicles: Encoding and thresholding approach'. Together they form a unique fingerprint.

Cite this