This dataset presents multiaxial vibration signals collected from a multirotor unmanned aerial vehicle (UAV) operating in hover mode for the purpose of blade fault diagnosis. Vibration measurements were recorded at the geometric center of the UAV, where the centerlines of the four rotor arms intersect, using a triaxial accelerometer. The dataset captures variations across the X, Y, and Z axes under different blade fault conditions, including healthy, minor imbalance, severe imbalance, and screw loosening scenarios. Each flight scenario was repeated under controlled conditions to ensure consistency and high-quality labeling. The resulting soft-labeled dataset includes time-domain signals from numerous test flights and has been used in multiple prior studies involving classical and deep learning-based fault classification techniques. This curated data collection provides a valuable resource for researchers in UAV health monitoring, vibration analysis, and machine learning-based fault diagnosis. The dataset is particularly useful for the development and benchmarking of signal processing pipelines and classification models aimed at identifying blade-level faults in multirotor UAV systems.

Applied Science Research Center Applied Science Private University Amman 11931 Jordan

Center for Scientific Research and Entrepreneurship Northern Border University Arar 73213 Saudi Arabia

ENET Centre CEET VSB Technical University of Ostrava Ostrava Czech Republic

Mechanical Engineering Department University of Technology Iraq Baghdad Iraq

University of Business and Technology College of Engineering Jeddah 21448 Saudi Arabia

See more in PubMed

Yang, Q. PubMed PMC

Wang, J., Yang, K., Wu, B. & Wang, J. Cooperative Path Planning for Persistent Surveillance in Large-Scale Environment with UAV-UGV System.

Cheng, K.

Huang, C.-H., Chen, Y.-C., Hsu, C.-Y., Yang, J.-Y. & Chang, C.-H. FPGA-based UAV and UGV for search and rescue applications: A case study.

Ai, S., Song, J., Cai, G. & Zhao, K. Active Fault-Tolerant Control for Quadrotor UAV against Sensor Fault Diagnosed by the Auto Sequential Random Forest.

Saied, M., Lussier, B., Fantoni, I., Shraim, H. & Francis, C. Fault Diagnosis and Fault-Tolerant Control of an Octorotor UAV using motors speeds measurements.

Iannace, G., Ciaburro, G. & Trematerra, A. Fault diagnosis for UAV blades using artificial neural network.

Liang, S. PubMed

Al-Haddad, L. A. PubMed PMC

Al-Haddad, L. A. & Jaber, A. A. An Intelligent Fault Diagnosis Approach for Multirotor UAVs Based on Deep Neural Network of Multi-Resolution Transform Features.

Al-Haddad, L. A. & Jaber, A. A. An Intelligent Quadcopter Unbalance Classification Method Based on Stochastic Gradient Descent Logistic Regression. in

Al-Haddad, L. A. & Jaber, A. A. Improved UAV blade unbalance prediction based on machine learning and ReliefF supreme feature ranking method.

Al-Haddad, L. A., Giernacki, W., Shandookh, A. A., Jaber, A. A. & Puchalski, R. Vibration Signal Processing for Multirotor UAVs Fault Diagnosis: Filtering or Multiresolution Analysis?

Al-Haddad, L. A. & Jaber, A. A. Influence of Operationally Consumed Propellers on Multirotor UAVs Airworthiness: Finite Element and Experimental Approach.

Al-Haddad, L. A., Jaber, A. A., Neranon, P. & Al-Haddad, S. A. Investigation of Frequency-Domain-Based Vibration Signal Analysis for UAV Unbalance Fault Classification.

Jaber, A. A. & Al-Haddad, L. A. Integration of Discrete Wavelet and Fast Fourier Transforms for Quadcopter Fault Diagnosis.

Kim, T., Kim, S. & Shin, H.-S. Data-driven diagnosis of multicopter thrust fault using supervised learning with inertial sensors.

Yaman, O., Yol, F. & Altinors, A. A fault detection method based on embedded feature extraction and SVM classification for UAV motors.

Baldini, A.

Sadhu, V., Anjum, K. & Pompili, D. On-board deep-learning-based unmanned aerial vehicle fault cause detection and classification via fpgas.

Al-Haddad, L. A.

Luttfi, A. Al-Haddad. Dataset on UAV Fault Diagnosis Using Vibrational Signals.

Demšar, J., Zupan, B., Leban, G. & Curk, T. Orange: From Experimental Machine Learning to Interactive Data Mining. in

Ferhi, W., Hadjila, M., Djillali, D. M. & Bouidaine, A. B. Machine Learning-based Classification of Diabetes Disease: A Case Study with Orange Data Mining.

Tiwari, R., Kumar, G. & Gunjan, V. K. Effect of Environment on Students Performance Through Orange Tool of Data Mining. in