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An Acoustic Fault Detection and Isolation System for Multirotor UAV


[ 1 ] Instytut Robotyki i Inteligencji Maszynowej, Wydział Automatyki, Robotyki i Elektrotechniki, Politechnika Poznańska | [ P ] employee | [ S ] student

Scientific discipline (Law 2.0)

[2.2] Automation, electronics and electrical engineering

Year of publication


Published in


Journal year: 2022 | Journal volume: vol. 15 | Journal number: iss. 11

Article type

scientific article

Publication language


  • UAV
  • fault detection
  • rotor
  • data-driven
  • acoustic

EN With the rising popularity of unmanned aerial vehicles (UAVs) and increasing variety of their applications, the task of providing reliable and robust control systems becomes significant. An active fault-tolerant control (FTC) scheme requires an effective fault detection and isolation (FDI) algorithm to provide information about the fault’s occurrence and its location. This work aims to present a prototype of a diagnostic system intended to recognize and identify broken blades of rotary wing UAVs. The solution is based on an analysis of acoustic emission recorded with an onboard microphone array paired with a lightweight yet powerful single-board computer. The standalone hardware of the FDI system was utilized to collect a wide and publicly available dataset of recordings in real-world experiments. The detection algorithm itself is a data-driven approach that makes use of an artificial neural network to classify characteristic features of acoustic signals. Fault signature is based on Mel Frequency Spectrum Coefficients. Furthermore, in the paper an extensive evaluation of the model’s parameters was performed. As a result, a highly accurate fault classifier was developed. The best models allow not only a detection of fault occurrence, but thanks to multichannel data provided with a microphone array, the location of the impaired rotor is reported, as well.

Date of online publication


Pages (from - to)

3955-1 - 3955-19





Article number: 3955

License type

CC BY (attribution alone)

Open Access Mode

open journal

Open Access Text Version

final published version

Release date


Date of Open Access to the publication

at the time of publication

Full text of article

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Points of MNiSW / journal


Impact Factor

3.004 [List 2020]

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