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Dataset for drone problem identification and severity estimation.

Swardiantara Silalahi1, Tohari Ahmad1, Hudan Studiawan1

  • 1Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia.

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Summary
This summary is machine-generated.

This study introduces DroSev, a new dataset for identifying drone issues and estimating their severity. It enables better drone health monitoring and predictive maintenance through log message analysis.

Keywords:
Drone datasetDrone forensicsInfrastructureLog analysisProblem identificationProblem severity

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Area of Science:

  • Drone technology
  • Data science
  • Machine learning

Background:

  • Drone flight logs contain critical information for operational safety.
  • Accurate problem identification and severity estimation are essential for drone maintenance.

Purpose of the Study:

  • To introduce DroSev, a novel dataset for drone problem identification and severity estimation.
  • To facilitate research in automated drone health monitoring and predictive maintenance.

Main Methods:

  • Acquired drone flight log messages from Mendeley Data and AirData.
  • Developed two subtasks: binary problem identification and multiclass problem severity classification.
  • Utilized stratified sampling for an 80:20 train-test split.

Main Results:

  • The DroSev dataset provides a comprehensive resource for drone log analysis.
  • The dataset supports both identification and severity assessment of drone-related problems.
  • Syntactical characteristics of the log messages are summarized.

Conclusions:

  • DroSev is a valuable resource for advancing drone safety and reliability.
  • The dataset can be used to train machine learning models for automated drone diagnostics.
  • Further research can leverage DroSev for improved drone operational management.