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RESEARCH PAPER
Vibration Signal Processing for Multirotor UAVs Fault Diagnosis: Filtering or Multiresolution Analysis?
 
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1
Training and Workshops Center, University Of Technology- Iraq, Iraq
 
2
Faculty of Automatic Control, Robotics and Electrical Engineering, Institute of Robotics and Machine Intelligence, Poznan University of Technology, Poland
 
3
Mechanical Engineering Department, University of Technology- Iraq, Iraq
 
These authors had equal contribution to this work
 
 
Submission date: 2023-10-22
 
 
Final revision date: 2023-11-11
 
 
Acceptance date: 2023-12-04
 
 
Online publication date: 2023-12-08
 
 
Publication date: 2023-12-08
 
 
Corresponding author
Radosław Puchalski   

Faculty of Automatic Control, Robotics and Electrical Engineering, Institute of Robotics and Machine Intelligence, Poznan University of Technology, 60-965 Poznan, Poland
 
 
Eksploatacja i Niezawodność – Maintenance and Reliability 2024;26(1):176318
 
HIGHLIGHTS
  • Comprehensive evaluation of Kalman filtering and DWT in UAV fault diagnosis.
  • Experimental setup includes vibration accelerometer and data acquisition system.
  • Finite element analysis determines optimal 1024 Hz sampling frequency.
  • DWT outperforms Kalman filtering in revealing intricate fault details.
  • Study contributes to state-of-the-art in multirotor UAV health monitoring.
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ABSTRACT
In the modern technological advancements, Unmanned Aerial Vehicles (UAVs) have emerged across diverse applications. As UAVs evolve, fault diagnosis witnessed great advancements, with signal processing methodologies taking center stage. This paper presents an assessment of vibration-based signal processing techniques, focusing on Kalman filtering (KF) and Discrete Wavelet Transform (DWT) multiresolution analysis. Experimental evaluation of healthy and faulty states in a quadcopter, using an accelerometer, are presented. The determination of the 1024 Hz sampling frequency is facilitated through finite element analysis of 20 mode shapes. KF exhibits commendable performance, successfully segregating faulty and healthy peaks within an acceptable range. While the six-level multi-decomposition unveils good explanations for fluctuations eluding KF. Ultimately, both KF and DWT showcase high-performance capabilities in fault diagnosis. However, DWT shows superior assessment precision, uncovering intricate details and facilitating a holistic understanding of fault-related characteristics.
FUNDING
This work was supported by Poznan University of Technology grant no. 0214/SBAD/0241.
eISSN:2956-3860
ISSN:1507-2711
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