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

Inbal Roshanski, Magenya Roshanski, Meir Kalech

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publication35th International Conference on Principles of Diagnosis and Resilient Systems, DX 2024
EditorsIngo Pill, Avraham Natan, Franz Wotawa
PublisherSchloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
ISBN (Electronic)9783959773560
DOIs
StatePublished - 26 Nov 2024
Event35th International Conference on Principles of Diagnosis and Resilient Systems, DX 2024 - Vienna, Austria
Duration: 4 Nov 20247 Nov 2024

Publication series

NameOpenAccess Series in Informatics
Volume125
ISSN (Print)2190-6807

Conference

Conference35th International Conference on Principles of Diagnosis and Resilient Systems, DX 2024
Country/TerritoryAustria
CityVienna
Period4/11/247/11/24

Keywords

  • Anomaly Detection
  • Correlation-Based Algorithms
  • Data-Driven Fault Detection
  • Drones
  • Sensor Data Analysis
  • Sensor Fault Detection

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Modeling and Simulation

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