An Acoustic Fault Detection and Isolation System for Multirotor UAV

Adam Bondyra, Marek Kołodziejczak, Radosław Kulikowski and Wojciech Giernacki
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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

Energies, 2022, vol. 15, issue 11, 1-19

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.

Keywords: UAV; fault detection; rotor; data-driven; acoustic (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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