Analysis of Diagnostic Capabilities for Degradation of Brushless Direct Current Motors Depending on Varying Simulation Data



Published Sep 4, 2023
Max Weigert


As the use of unmanned aerial vehicles (UAVs) becomes more widespread and their missions more complex, the need for safety measures for their technical components is also increasing. Among the components that are critical for the operation of UAVs, Brushless Direct Current (BLDC) motors are particularly important. This is due to their compact design and low number of wear parts, which make them well-suited for use in UAVs. In this work, test rig and simulation data of BLDC motors degradation are utilized to investigate the capabilities and limitations of different machine learning algorithms. For this purpose, suitable features representing the motor behavior are discussed. Classification and regression tasks are applied to analyze both the fault type and the degradation progress. The simulated data allows for an assessment of the influence of noise and degradation progress on the diagnosis performance. Furthermore, characteristics of various fault types and the representation of their degradation process in the simulation are discussed. The database and the derived features are shared publicly.

Abstract 146 | PDF Downloads 158



Brushless Direct Current Motor, Diagnosis, Simulation

Akiba, T.; Sano, S.; Yanase, T.; Ohta, T.; Koyama, M. (2019): Optuna: A Next-generation Hyperparameter Optimization Framework.

AlShorman, O.; Irfan, M.; Saad, N.; Zhen, D.; Haider, N.; Glowacz, A.; AlShorman, A. (2020): A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor. In Shock and Vibration 2020, pp. 1–20. DOI: 10.1155/2020/8843759.

Bertolino, A. C.; Martin, A. d.; Jacazio, G.; Sorli, M. (2023): Design and Preliminary Performance Assessment of a PHM System for Electromechanical Flight Control Actuators. In Aerospace 10 (4), p. 335. DOI: 10.3390/aerospace10040335.

Chen, T.; Guestrin, C. (2016): XGBoost: A Scalable Tree Boosting System. DOI: 10.48550/arXiv.1603.02754.

Gupta, A.; Jayaraman, K.; Reddy, R. S. (2021): Performance Analysis and Fault Modelling of High Resistance Contact in Brushless DC Motor Drive. In : IECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society. Toronto, ON, Canada, 13.10.2021 - 16.10.2021: IEEE, pp. 1–6.

Hasan, R.; Chu, C. (2022): Noise in Datasets: What Are the Impacts on Classification Performance? In : Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, pp. 163–170.

Jiang, Z.; Han, Q.; Xu, X. (2020): Fault Diagnosis of Planetary Gearbox Based on Motor Current Signal Analysis. In Shock and Vibration 2020, pp. 1–13. DOI: 10.1155/2020/8854776.

Kudelina, K.; Asad, B.; Vaimann, T.; Rassolkin, A.; Kallaste, A.; Lukichev, D.V. (2020): Main Faults and Diagnostic Possibilities of BLDC Motors. In : 2020 27th International Workshop on Electric Drives: MPEI Department of Electric Drives 90th Anniversary (IWED). Moscow, Russia, 27.01.2020 - 30.01.2020: IEEE, pp. 1–6.

Lei, Y.; He, Z.; Zi, Y.; Hu, Q. (2007): Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs. In Mechanical Systems and Signal Processing 21 (5), pp. 2280–2294. DOI: 10.1016/j.ymssp.2006.11.003.

Nectoux, P.; Gouriveau, R.; Medjaher, K.; Ramasso, E.; Chebel-Morello, B.; Zerhouni, N.; Varnier, C. (2012): PRONOSTIA: An experimental platform for bearings accelerated degradation tests. In Proceedings of the IEEE International Conference on Prognostics and Health Management. hal-00719503

Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O. et al. (2012): Scikit-learn: Machine Learning in Python. DOI: 10.48550/arXiv.1201.0490.

Shifat, T. A.; Hur, J. W. (2020): An Effective Stator Fault Diagnosis Framework of BLDC Motor Based on Vibration and Current Signals. In IEEE Access 8, pp. 106968–106981. DOI: 10.1109/ACCESS.2020.3000856.

Siddiolo, A. M.; Buderath, M. (2018): Development of a Prognostic Framework. In 10th International Symposium on NDT in Aerospace. Available online at

Weigert, M. (2022): Approach to Condition Monitoring of BLDC Motors with Experimentally Validated Simulation Data. In PHME_CONF 7 (1), pp. 521–529. DOI: 10.36001/phme.2022.v7i1.3357.

Wolfram, D.; Vogel, F.; Stauder, D. (2018): Condition monitoring for flight performance estimation of small multirotor unmanned aerial vehicles. In : 2018 IEEE Aerospace Conference. Big Sky, MT, 03.03.2018 - 10.03.2018: IEEE, pp. 1–17
Regular Session Papers