Experimental fault detection of a BLDC motor

Resumen: This work presents an experimental fault detection (FD) method for a Brushless Direct Current (BLDC) motor. The proposed scheme allowed to detect incipient short-circuit stator faults based on stator current modulus analysis using digital signal processing techniques (FFT and DWT). The FD algorithm was validated on a test rig with an in-wheel BLDC motor for light electric vehicles.

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Rodolfo Jauregui-Acevedo, Francisco Javier Villalobos Piña, Ricardo Alvarez-Salas, Carlos Humberto Saucedo Zárate, Héctor Méndez-Azúa & Jose Antonio Alvarez Salas. Experimental fault detection of a BLDC motor. Memorias del Congreso Nacional de Control Automático, pp. 1-5, 2020.

Palabras clave

Fault detection, BLDC motor, FFT, DWT, test rig

• Racewicz, S., Kazimierczuk, P., Kolator, B. A. and Olszewski, A. (2018). Use of 3 kW BLDC motor for light two-wheeled electric vehicle construction. IOP Conference Series: Materials Science and Engineering, 421, pp. 042-067.
• Faiz J. and Jafari A. (2018). Interturn fault diagnosis in brushless direct current motors — A review. IEEE International Conference on Industrial Technology (ICIT), Lyon, pp. 437-444.
• Xue H., Zhou H., Wang M., Li Z. and Jiang H. (2019). Using rotating speed monitoring for leakage fault diagnosis of in-wheel motor. Journal of Applied Science and Engineering, 22, 2.
• Huang C., Lei F., Han X. and Zhang Z.(2019). Determination of modeling parameters for a brushless dc motor that satisfies the power performance of an electric vehicle. Measurement and Control, 52, 7-8, pp.765-774.
• Prakash S. and Sherine S.(2017). Control of BLDC motor powered electric vehicle using indirect vector control and sliding mode observer International Journal of Pure and Applied Mathematics, 116, 19, pp. 295-300.
• Tashakori A., Hassanudeen M. and Ektesabi M.(2015). FPGA based controller drive of BLDC motor using digital PWM technique. IEEE 11th International Conference on Power Electronics and Drive Systems, Sydney, Australia, pp. 658-662.
• Liu H. and Zhang H. (2016). An improved DTC for in-wheel BLDC motors in micro all-electric vehicles. Automatika, 57, 3, pp.648-659.
• Zhang D., Liu G., Zhou H. and Zhao W.(2018). Adaptive sliding mode fault-tolerant coordination control for four-wheel independently driven electric vehicles. in IEEE Transactions on Industrial Electronics, vol. 65, no. 11, pp. 9090-9100.
• Mitronikas E., Papathanasopoulos D., Athanasiou G. and Tsotoulidis S.(2017). Hall-effect sensor fault identification in brushless dc motor drives using wavelets. IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), Tinos, Greece, pp. 434-440.
• Imoru O., Bhaskar M. A., Abdul-Ganiyu A.J. and Hamam Y. (2017). Diagnosis of stator shorted-turn faults in induction machines using discrete wavelet transform. African Journal of Science, Technology, Innovation and Development, vol. 9, no. 3, pp. 349-355.
• Bulgakov A. and Kruglova T. (2017). Intelligent method for fault finding of electric drive based on wavelet analysis of its electrical and mechanical parameters. International Conference on Industrial Engineering, ICIE, Procedia Engineering, vol. 206, pp. 929 – 935.
• Kruglova T., Shmelev I., Bulgakov A. and Ryzhenkov S.(2018). Signal-based intelligent diagnostic method for BLDC motors. X International Conference on Electrical Power Drive Systems (ICEPDS), Novocherkassk, pp. 1-5.
• Hosseini S. M., Hosseini F. and Abedi M. (2019). Stator fault diagnosis of a BLDC motor based on discrete wavelet analysis using Adams simulation. SN Applied Sciences, vol. 1, 1406.
• Lee S. and Hur J.(2017). Detection technique for stator inter-turn faults in BLDC motors based on third-harmonic components of line currents. in IEEE Transactions on Industry Applications, vol. 53, no. 1, pp. 143-150.
• Sardana G., Turk N. and Deswal S. (2019). Eccentricity fault diagnosis in BLDC motor using finite element and frequency research. International Journal of Recent Technology and Engineering (IJRTE), vol. 8, no.211.
• Papathanasopoulos D., Athanasiou G., Tsotoulidis S. and Mitronikas E. (2017). Fault identification on Hall-effect sensors positioning in brushless dc motor drives via a fuzzy inference system. IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), Tinos, Greece, pp. 311-317.
• Simsir M., Bayır R. and Uyaro˘glu Y. (2016). Real-time monitoring and fault diagnosis of a low power hub motor using feedforward neural network. Computational Intelligence and Neuroscience, vol. 2016, pp.13.
• Xue H., Zhou J., Wang M., Li Z. and Jiang H.(2019). Using rotating speed monitoring for leakage fault diagnosis of in-wheel motor. Journal of Applied Science and Engineering, vol. 22, no. 2, pp. 307-314.
• Weeks M. (2007). Digital signals processing using matlab and wavelets. Infinite Science Press LCC.
• Walker J. S. (2008). A primer on wavelets and their scientific applications. Second edition, Chapman & Hall/CRC.
• Drif M. and Cardoso A. J. M. (2008) Airgap-Eccentricity Fault Diagnosis, in Three-Phase Induction Motors, by the Complex Apparent Power Signature Analysis, in IEEE Transactions on Industrial Electronics, vol. 55, no. 3, pp. 1404-1410.