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DOI: 10.4230/OASIcs.DX.2024.17

Real-Time Sensor Fault Detection in Drones: A Correlation-Based Algorithmic Approach

Authors: Inbal Roshanski, Magenya Roshanski, and Meir Kalech

Published in: OASIcs, Volume 125, 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)

Abstract

Drones, or unmanned aerial vehicles (UAVs), are becoming increasingly vital across various industries, where their reliable operation is crucial for safety and efficiency. Ensuring this reliability requires the early detection of sensor-related faults, which are critical for maintaining the performance and safety of UAVs. This study addresses this challenge by leveraging real-world data from an Aero-Sentinel Military UAV Sentinel G2 quadcopter. The data was collected through a collaboration with Maris-Tech Ltd, using their advanced Mercury Nano system to capture detailed communication between the drone and its control unit. A set of correlation-based algorithms was developed and evaluated, specifically tailored to address the unique complexities of drone sensor data, which is often influenced by environmental factors. Among the algorithms tested, two novel methods emerged as particularly effective, demonstrating significant improvement compared to previous methods, in fault detection accuracy. These methods, designed to accurately identify and predict sensor malfunctions, offer a robust solution for enhancing the reliability and safety of UAV operations.

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Inbal Roshanski, Magenya Roshanski, and Meir Kalech. Real-Time Sensor Fault Detection in Drones: A Correlation-Based Algorithmic Approach. In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 17:1-17:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{roshanski_et_al:OASIcs.DX.2024.17,
  author =	{Roshanski, Inbal and Roshanski, Magenya and Kalech, Meir},
  title =	{{Real-Time Sensor Fault Detection in Drones: A Correlation-Based Algorithmic Approach}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{17:1--17:20},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-356-0},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{125},
  editor =	{Pill, Ingo and Natan, Avraham and Wotawa, Franz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2024.17},
  URN =		{urn:nbn:de:0030-drops-221092},
  doi =		{10.4230/OASIcs.DX.2024.17},
  annote =	{Keywords: Drones, Sensor Fault Detection, Correlation-Based Algorithms, Sensor Data Analysis, Anomaly Detection, Data-Driven Fault Detection}
}

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Real-Time Sensor Fault Detection in Drones: A Correlation-Based Algorithmic Approach

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