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An Acoustic Fault Detection and Isolation System for Multirotor UAV

Author

Listed:
  • Adam Bondyra

    (Faculty of Automatic Control, Robotics and Electrical Engineering, Institute of Robotics and Machine Intelligence, Poznan University of Technology, ul. Piotrowo 3a, 60-965 Poznan, Poland)

  • Marek Kołodziejczak

    (Faculty of Automatic Control, Robotics and Electrical Engineering, Institute of Robotics and Machine Intelligence, Poznan University of Technology, ul. Piotrowo 3a, 60-965 Poznan, Poland)

  • Radosław Kulikowski

    (Faculty of Automatic Control, Robotics and Electrical Engineering, Institute of Robotics and Machine Intelligence, Poznan University of Technology, ul. Piotrowo 3a, 60-965 Poznan, Poland)

  • Wojciech Giernacki

    (Faculty of Automatic Control, Robotics and Electrical Engineering, Institute of Robotics and Machine Intelligence, Poznan University of Technology, ul. Piotrowo 3a, 60-965 Poznan, Poland)

Abstract

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.

Suggested Citation

  • Adam Bondyra & Marek Kołodziejczak & Radosław Kulikowski & Wojciech Giernacki, 2022. "An Acoustic Fault Detection and Isolation System for Multirotor UAV," Energies, MDPI, vol. 15(11), pages 1-19, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:11:p:3955-:d:825480
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    References listed on IDEAS

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    1. Qingqing Zhai & Zhi-Sheng Ye, 2020. "How reliable should military UAVs be?," IISE Transactions, Taylor & Francis Journals, vol. 52(11), pages 1234-1245, November.
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    Cited by:

    1. Miguel Louro & Luís Ferreira, 2022. "Estimation of Underground MV Network Failure Types by Applying Machine Learning Methods to Indirect Observations," Energies, MDPI, vol. 15(17), pages 1-15, August.
    2. Wojciech Giernacki, 2022. "Minimum Energy Control of Quadrotor UAV: Synthesis and Performance Analysis of Control System with Neurobiologically Inspired Intelligent Controller (BELBIC)," Energies, MDPI, vol. 15(20), pages 1-23, October.

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