Fault Diagnosis in the Brushless Direct Current Drive Using Hybrid Machine Learning Models
Article Sidebar
Main Article Content
Abstract
The brushless direct current (BLDC) motor drive is gaining popularity due to its excellent controllability and high efficiency. This paper introduces a fault diagnosis method for open circuit (OC) and short circuit (SC) BLDC motor drives using a hybrid classifier with hybrid optimization. Features such as current, voltage, speed, and torque are considered as the training data. The features are extracted by discrete wavelet transform (DWT) and then employed to train the classifiers to distinguish between fault types and values of response parameters using the support vector machine and Naive Bayes classifier (SVM-NB). To further improve the performance of the system, hybrid chaotic particle swarm optimization (CPSO) algorithms and teaching-learning-based optimization (TLBO) are used. This method is capable of detecting and recognizing the type of faults in the BLDC motor. The developed approach is implemented on the MATLAB/SIMULINK for OC, SC, and no-fault conditions. These hybrid algorithms provide better performance compared to existing approaches with respect to sensitivity, accuracy, and specificity. This improved model achieves about 98.8% accuracy.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
- Creative Commons Copyright License
The journal allows readers to download and share all published articles as long as they properly cite such articles; however, they cannot change them or use them commercially. This is classified as CC BY-NC-ND for the creative commons license.
- Retention of Copyright and Publishing Rights
The journal allows the authors of the published articles to hold copyrights and publishing rights without restrictions.
References
S. Lu and X. Wang, “A new methodology to estimate the rotating phase of a BLDC motor with its application in variable-speed bearing fault diagnosis,” IEEE Transactions on Power Electronics, vol. 33, no. 4, pp. 3399–3410, Apr. 2018.
J. Choi, J.-H. Lee, Y.-G. Jung, and H. Park, “Enhanced efficiency of the brushless direct current motor by introducing air flow for cooling,” Heat and Mass Transfer, vol. 56, no. 6, pp. 1825–1831, Jun. 2020.
T. A. Shifat and J. W. Hur, “An effective stator fault diagnosis framework of BLDC motor based on vibration and current signals,” IEEE Access, vol. 8, pp. 106968–106981, 2020.
C. He and T. Wu, “Permanent magnet brushless DC motor and mechanical structure design for the electric impact wrench system,” Energies, vol. 11, no. 6, p. 1360, 2018.
K. Kudelina, B. Asad, T. Vaimann, A. Belahcen, A. Rassõlkin, A. Kallaste, and D. V. Lukichev, “Bearing fault analysis of BLDC motor for electric scooter application,” Designs, vol. 4, no. 4, p. 42, 2020.
R. L. V. Medeiros, A. C. L. Filho, J. G. G. S. Ramos, T. P. Nascimento, and A. V. Brito, “A novel approach for speed and failure detection in brushless DC motors based on chaos,” IEEE Transactions on Industrial Electronics, vol. 66, no. 11, pp. 8751–8759, Nov. 2019.
D. A. Papathanasopoulos and E. D. Mitronikas, “Fault tolerant control of a brushless DC motor with defective position sensors,” in 2018 XIII International Conference on Electrical Machines (ICEM), 2018, pp. 1503–1509.
H. Wang, S. Lu, G. Qian, J. Ding, Y. Liu, and Q. Wang, “A two-step strategy for online fault detection of high-resistance connection in BLDC motor,” IEEE Transactions on Power Electronics, vol. 35, no. 3, pp. 3043–3053, Mar. 2020.
A. G. Espinosa, J. A. Rosero, J. Cusido, L. Romeral, and J. A. Ortega, “Fault detection by means of hilbert–huang transform of the stator current in a PMSM with demagnetization,” IEEE Transactions on Energy Conversion, vol. 25, no. 2, pp. 312–318, Jun. 2010.
S. M. R. Balaji, C. Muniraj, and M. N, “Wavelet transform based fault diagnosis of BLDC motor drive,” Indonesian Journal of Electrical Engineering, vol. 14, no. 3, pp. 434–440, Jun. 2015.
V. M. Fico, M. A. M. Prats, and A. L. R. Vazquez, “Brushless DC motors failure detection using the continuous wavelet transform,” Aerotecnica Missili & Spazio, vol. 93, no. 3/4, pp. 61–67, Jul. 2014.
A. A. Obed and A. K. Kadhim, “Speed and current limiting control strategies for BLDC motor drive system: A comparative study,” International Journal of Advanced Engineering Research and Science, vol. 5, no. 2, pp. 119–130, Feb. 2018.
S. R. Vippala, S. Bhat, and A. A. Reddy, “Condition monitoring of BLDC motor using short time Fourier transform,” in 2021 IEEE Second International Conference on Control, Measurement and Instrumentation (CMI), 2021, pp. 110–115.
C.-Y. Lee and T.-A. Le, “Optimised approach of feature selection based on genetic and binary state transition algorithm in the classification of bearing fault in BLDC motor,” IET Electric Power Applications, vol. 14, no. 13, pp. 2598–2608, Dec. 2020.
F. Alvarez-Gonzalez, A. Griffo, and B. Wang, “Permanent magnet synchronous machine stator windings fault detection by hilbert–huang transform,” The Journal of Engineering, vol. 2019, no. 17, pp. 3505–3509, Jun. 2019.
Q. Wu, Z. Ma, G. Xu, S. Li, and D. Chen, “A novel neural network classifier using beetle antennae search algorithm for pattern classification,” IEEE Access, vol. 7, pp. 64686–64696, 2019.
P. Ghamisi and J. A. Benediktsson, “Feature selection based on hybridization of genetic algorithm and particle swarm optimization,” IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 2, pp. 309–313, Feb. 2015.
M. Unal, M. Onat, M. Demetgul, and H. Kucuk, “Fault diagnosis of rolling bearings using a genetic algorithm optimized neural network,” Measurement, vol. 58, pp. 187–196, Dec. 2014.
R. Ilka and S. A. Gholamian, “Application of artificial bee colony algorithm and finite element analysis for optimum design of brushless permanent magnet motor,” IIUM Engineering Journal, vol. 13, no. 1, Apr. 2012.
S. K. Pandey, C. Bera, and S. S. Dwivedi, “Design of robust PID controller for DC motor using TLBO algorithm,” in 2020 IEEE International Conference on Advances and Developments in Electrical and Electronics Engineering (ICADEE), 2020.
O. Zandi and J. Poshtan, “Fault diagnosis of brushless DC motors using built-in hall sensors,” IEEE Sensors Journal, vol. 19, no. 18, pp. 8183–8190, Sep. 2019.
E. Aker, M. L. Othman, V. Veerasamy, I. bin Aris, N. I. A. Wahab, and H. Hizam, “Fault detection and classification of shunt compensated transmission line using discrete wavelet transform and naive bayes classifier,” Energies, vol. 13, no. 1, p. 243, Jan. 2020.
S. M. Hosseini, F. Hosseini, and M. Abedi, “Stator fault diagnosis of a BLDC motor based on discrete wavelet analysis using ADAMS simulation,” SN Applied Sciences, vol. 1, no. 11, p. 1406, 2019.
T. A. Shifat and J.-W. Hur, “ANN assisted multi sensor information fusion for BLDC motor fault diagnosis,” IEEE Access, vol. 9, pp. 9429–9441, 2021.
H. Cherif, A. Menacer, B. Bessam, and R. Kechida, “Stator inter turns fault detection using discrete wavelet transform,” in 2015 IEEE 10th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), 2015, pp. 138–142.
W. Feng, J. Sun, L. Zhang, C. Cao, and Q. Yang, “A support vector machine based naive bayes algorithm for spam filtering,” in 2016 IEEE 35th International Performance Computing and Communications Conference (IPCCC), 2016.
K. Korovkinas, P. Danėnas, and G. Garšva, “SVM and naïve bayes classification ensemble method for sentiment analysis,” Baltic Journal of Modern Computing, vol. 5, no. 4, pp. 398–409, Dec. 2017.
F. Zou, D. Chen, and J. Wang, “An improved Teaching-Learning-Based Optimization with the social character of PSO for global optimization,” Computational Intelligence and Neuroscience, vol. 2016, p. 4561507, 2016.
R. Rao, V. Savsani, and D. Vakharia, “Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems,” Computer-Aided Design, vol. 43, no. 3, pp. 303–315, Mar. 2011.