Document Type : Research Paper


1 Training and Workshop Center

2 Mechanical Engineering Department, University of Technology, Baghdad

3 3Department of Mechanical and Mechatronics Engineering, Faculty of Engineering, Prince of Songkla University, Songkhla 90112, Thailand

4 Civil Engineering Department, University of Technology-Iraq, Alsinaa Street 52, 10066 Baghdad, Iraq


The flying reliability of Unmanned Aerial Vehicles (UAVs) and flying robots, which directly determines the operational degree of safety, is becoming more important in recent intelligent decades. Reliability and a high level of safety are critical for autonomously controlled flying robots, especially in transportation and entertainment applications. Subsequently, monitoring UAV health is crucial and essential for system safety, cost savings, and excellent dependability. The development of numerous monitoring strategies has resulted from the requirement for a simple and accurate unbalance classification procedure. This paper provides an Unbalance Classification and Isolation (UCI) system for multirotor UAV propeller impairments. The technique is based on the processing of signal vectors from a vibration sensor positioned in the lines of the intersection of a modern-day drone's four propulsion units, which supply data for the Fast Fourier Transform (FFT) feature extraction. To identify and locate broken blades, characteristic fault signatures collected from vibration signals are employed and displayed in real-time on the programming platform. A noticeable maximum frequency shifting percentage value of 4.2% is acquired when deviating from a healthy state. The results reveal that identifying and isolating defective rotor states has high sensitivity and outperforms current studies in regard to unbalance classification of UAVs. The adopted technique is an efficient and low-cost solution that can be implemented in any multirotor UAV.

Graphical Abstract


  • Small UAV drone vibration signals are gathered utilizing the LabVIEW DAQ assistant.
  • Spectral analysis of Fast Fourier Transform signal processing technique in x, y, and z was performed.
  • Unbalance fault classification based on FFT peak frequencies of rolling, pitching, and yawing in a DJI mini 2 combo was used.
  •  Real-time UAV health monitoring method was discussed and explained accordingly.


Main Subjects

B. Alzahrani, O. S. Oubbati, A. Barnawi, M. Atiquzzaman, D. Alghazzawi, UAV assistance paradigm: State-of-the-art in applications and challenges, J. Network Comput. Appl.,166 (2020) 102706.
[2] A. Abdulkareem, A. Humod, O. Ahmed, Fault Detection and Fault Tolerant Control for Anti-lock Braking Systems (ABS) Speed Sensors by Using Neural Networks, Eng. Technol. J., 41 (2022) 1–12.
[3] S. Jawad and A. Jaber, Bearings Health Monitoring Based on Frequency-Domain Vibration Signals Analysis, Eng. Technol. J., 41 (2022) 86–95.
[4] K. Jalal, L. Abd alameer, Fault Diagnosis in Wind Power System Based on Intelligent Techniques, Eng. Technol. J., 36 (2018)1201–1207.
[5] A. Bondyra, M. Kołodziejczak, R. Kulikowski, W. Giernacki, An Acoustic Fault Detection and Isolation System for Multirotor UAV, Energies (Basel), 15 (2022) 3955.
[6] A. Bondyra, P. Gasior, S. Gardecki, A. Kasinski, Development of the Sensory Network for the Vibration-based Fault Detection and Isolation in the Multirotor UAV Propulsion System, in ICINCO 2018 - Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics, 2 (2018) 102–109.
[7] A. Bondyra, P. Gasior, S. Gardecki, A. Kasiński, Fault diagnosis and condition monitoring of UAV rotor using signal processing, in 2017 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA),(2017) 233–238.
[8] S. Yoon, S. Kim, J. Bae, Y. Kim, E. Kim, Experimental evaluation of fault diagnosis in a skew-configured UAV sensor system, Control Eng Pract, 19 (2011) 158–173.
[9] R. Puchalski, W. Giernacki, UAV Fault Detection Methods, State-of-the-Art, Drones, 6 (2022) 330.
[10] G. K. Fourlas, G. C. Karras, A survey on fault diagnosis and fault-tolerant control methods for unmanned aerial vehicles †, Machines, 9 (2021) 197.
[11] G. K. Fourlas, G. C. Karras, A Survey on Fault Diagnosis Methods for UAVs, in 2021 International Conference on Unmanned Aircraft Systems (ICUAS), (2021) 394–403. https://doi.10.1109/ICUAS51884.2021.9476733.
[12] I. Sadeghzadeh, Y. Zhang, A Review on Fault-Tolerant Control for Unmanned Aerial Vehicles (UAVs), in Infotech@Aerospace 2011, American Institute of Aeronautics and Astronautics, 2011.
[13] D. Li, Y. Wang, J. Wang, C. Wang, Y. Duan, Recent advances in sensor fault diagnosis: A review, Sens. Actuator A Phys., 309 (2020) 111990.
[14] H. Shraim, A. Awada, R. Youness, A survey on quadrotors: Configurations, modeling and identification, control, collision avoidance, fault diagnosis and tolerant control, IEEE Aerosp. Electron. Syst. Mag., 33 (2018) 14–33. https://doi.10.1109/MAES.2018.160246.
[15] S. Xiang, L. Yang, Y. Wang, Robust and Reversible Audio Watermarking by Modifying Statistical Features in Time Domain, Adv. Multimedia., 2017 (2017) 1-10.
[16] M. M. Tahir, A. Q. Khan, N. Iqbal, A. Hussain, S. Badshah, Enhancing Fault Classification Accuracy of Ball Bearing Using Central Tendency Based Time Domain Features, IEEE Access, 5 (2017) 72–83.
[17] B. Li, Z. Jiang, J. Chen, Performance of the Multiscale Sparse Fast Fourier Transform Algorithm, Circuits Syst Signal Process, 41 (2022) 4547–4569.
[18] P. Xia, H. Zhou, H. Sun, Q. Sun, R. Griffiths, Research on a Fiber Optic Oxygen Sensor Based on All-Phase Fast Fourier Transform (apFFT) Phase Detection, Sensors, 22 (2022).
[19] M. H. M. Ghazali, W. Rahiman, An Investigation of the Reliability of Different Types of Sensors in the Real-Time Vibration-Based Anomaly Inspection in Drone, Sensors, 22 (2022) 6015.
[20] Sundararajan, D. (2023).The Discrete Fourier Transform, in Signals and Systems: A Practical Approach, D. Sundararajan, Ed. Cham: Springer Nature Switzerland, pp 125–160.
[21] M. Stanković, M. M. Mirza, U. Karabiyik, UAV forensics: DJI mini 2 case study, Drones, 5 (2021) 49.
[22] L. A. Al-Haddad, A. A. Jaber, An Intelligent Fault Diagnosis Approach for Multirotor UAVs Based on Deep Neural Network of Multi-Resolution Transform Features, Drones, 7 (2023) 82.