Search for Author, Title, Keyword
RESEARCH PAPER
Vibration Signal Processing for Multirotor UAVs Fault Diagnosis: Filtering or Multiresolution Analysis?
 
More details
Hide details
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.
KEYWORDS
TOPICS
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.
 
CITATIONS (7):
1.
Three-dimensional analysis of steel beam-column bolted connections
Sinan A. Al-Haddad, Mohammed Y. Fattah, Thamir K. Al-Azawi, Luttfi A. Al-Haddad
Open Engineering
 
2.
PADRE – A Repository for Research on Fault Detection and Isolation of Unmanned Aerial Vehicle Propellers
Radosław Puchalski, Quang Ha, Wojciech Giernacki, Huynh Anh Duy Nguyen, Lanh Van Nguyen
Journal of Intelligent & Robotic Systems
 
3.
Failure Analysis in Predictive Maintenance: Belt Drive Diagnostics with Expert Systems and Taguchi Method for Unconventional Vibration Features
Ahmed Adnan Shandookh, Ahmed Ali Farhan Ogaili, Luttfi A. Al-Haddad
Heliyon
 
4.
Naïve Bayes algorithm for timely fault diagnosis in helical gear transmissions using vibration signal analysis
Ahmed Ghazi Abdulameer, Ahmed Salman Hammood, Fawaz Mohammed Abdulwahed, Abdullah Abdulqader Ayyash
International Journal on Interactive Design and Manufacturing (IJIDeM)
 
5.
UAV propeller fault diagnosis using deep learning of non-traditional χ2-selected Taguchi method-tested Lempel–Ziv complexity and Teager–Kaiser energy features
Luttfi A. Al-Haddad, Wojciech Giernacki, Ali Basem, Zeashan Hameed Khan, Alaa Abdulhady Jaber, Sinan A. Al-Haddad
Scientific Reports
 
6.
Protocol for UAV fault diagnosis using signal processing and machine learning
Luttfi A. Al-Haddad, Alaa Abdulhady Jaber, Nibras M. Mahdi, Sinan A. Al-Haddad, Mustafa I. Al-Karkhi, Zainab T. Al-Sharify, Ahmed Ali Farhan Ogaili
STAR Protocols
 
7.
Towards dental diagnostic systems: Synergizing wavelet transform with generative adversarial networks for enhanced image data fusion
Abdullah A. Al-Haddad, Luttfi A. Al-Haddad, Sinan A. Al-Haddad, Alaa Abdulhady Jaber, Zeashan Hameed Khan, Hafiz Zia Ur Rehman
Computers in Biology and Medicine
 
eISSN:2956-3860
ISSN:1507-2711
Journals System - logo
Scroll to top