Classification of Inter-Turn Short-Circuit Fault Severity in Permanent Magnet Synchronous Motors Using Decision Tree and Bayesian Neural Network
Subject Areas : electrical and computer engineeringAbbas Darvishi 1 , Seyed Mohsen Seyed Moosavi 2 * , Behzad Moshiri 3
1 - Faculty of Elec. Eng., Ahvaz Branch, Islamic Azad University, Ahvaz, Iran
2 - Faculty of Elec. Eng., Ahvaz Branch, Islamic Azad University, Ahvaz, Iran
3 - School of Elec. and Comp. Eng., University of Tehran, Tehran, Iran
Keywords: Inter-turn short-circuit, permanent magnet synchronous motor, feature extraction, feature selection, decision tree, Bayesian neural network.,
Abstract :
This paper investigates the identification of inter-turn short-circuit fault severity in a 3-kilowatt permanent magnet synchronous motor using a decision tree and a deep Bayesian neural network. The primary dataset includes three-phase current signals under both healthy and faulty conditions, covering six fault severity levels. A preprocessing stage is conducted to analyze the data in time and frequency domains using discrete wavelet transform and power spectral density analysis. To reduce the dimensionality of the feature space, statistical indicators such as mean, standard deviation, kurtosis, and skewness are initially extracted. Kernel principal component analysis is then employed to identify the most salient features. A decision tree algorithm is trained to detect motor fault conditions. Finally, a deep Bayesian neural network is applied to classify the severity of the inter-turn short-circuit fault. The proposed algorithm’s performance is evaluated in terms of accuracy, precision, recall, and F1-score, considering varying numbers of selected dominant features.
[1] E. A. Bhuiyan, et al., "A survey on fault diagnosis and fault tolerant methodologies for permanent magnet synchronous machines," Int. J. Autom. Comput., vol. 17, pp. 763-787, 2020.
[2] A. Kilic and F. Weiss, "Early detection of PMSM faults at static and dynamic operating points by using artificial neural network for automated electric vehicle," J. Braz. Soc. Mech. Sci. Eng., vol. 45, no. 9, Article ID: 493, 2023.
[3] X. Chen, P. Qin, Y. Chen, J. Zhao, W. Li, Y. Mao, and T. Zhao, "Inter-turn short circuit fault diagnosis of PMSM," Electronics, vol. 11, no. 10, p. 1576, 2022.
[4] D. Wang and Y. Chen, "Fault-tolerant control of coil inter-turn short-circuit in five-phase permanent magnet synchronous motor," Energies, vol. 13, no. 21, Article ID: 5669, Nov. 1 2020.
[5] Y. Qin, G. J. Li, C. Jia, and P. McKeever, "Investigation of inter-turn short-circuit fault of PM machines using PWM voltage-based modeling," IEEE Trans. Transp. Electrific., vol. 10, no. 1, pp. 1324-1334, 2023.
[6] G. Forstner, A. Kugi, and W. Kemmetmüller, "Model-based fault identification of inter-turn winding short circuits in PMSM," in Proc. Int. Conf. Elect. Mach., vol. 1, pp. 1390-1396, Gothenburg, Sweden, 23-26 Aug. 2020.
[7] D. Niu and D. Song, "Model-based robust fault diagnosis of incipient ITSC for PMSM in elevator traction system," IEEE Trans. Instrum. Meas., vol. 72, Article ID: 3533512, 2023.
[8] A. Sarikhani and O. A. Mohammed, "Inter-turn fault detection in PM synchronous machines by physics-based back electromotive force estimation," IEEE Trans. Ind. Electron., vol. 60, no. 8, pp. 3472-3484, Aug. 2012.
[9] M. Fitouri, Y. Bensalem, and M. N. Abdelkrim, "Modeling and detection of the short-circuit fault in PMSM using finite element analysis," IFAC-PapersOnLine, vol. 49, no. 12, pp. 1418-1423, 2016.
[10] Y. Qi, E. Bostanci, V. Gurusamy, and B. Akin, "A comprehensive analysis of short-circuit current behavior in PMSM interturn short-circuit faults," IEEE Trans. Power Electron., vol. 33, no. 12, pp. 10784-10793, Dec. 2018.
[11] Y. Li, Y. Wang, Y. Zhang, and J. Zhang, "Diagnosis of inter-turn short circuit of permanent magnet synchronous motor based on deep learning and small fault samples," Neurocomputing, vol. 442, pp. 348-358, Jun. 2021.
[12] Y. Li, et al., "A fault diagnosis method based on an improved deep Q-network for the inter-turn short circuits of a permanent magnet synchronous motor," IEEE Trans. Transp. Electrific., vol. 10, no. 2, pp. 3870-3887, Jun. 2024.
[13] M. Skowron, T. Orlowska-Kowalska, and C. T. Kowalski, "Detection of permanent magnet damage of PMSM drive based on direct analysis of the stator phase currents using convolutional neural network," IEEE Trans. Ind. Electron., vol. 69, no. 12, pp. 13665-13675, Dec. 2022.
[14] X. Wu, Y. Geng, M. Li, W. Wang, and M. Tu, "Inter-turn short circuit diagnosis of permanent magnet synchronous motor based on Siamese convolutional neural network under small fault samples," IEEE Sensors J., vol. 24, no. 16, pp. 26982-26993, 15 Aug., 2024.
[15] Q. Chen, X. Dai, X. Song, and G. Liu, "ITSC fault diagnosis for five phase permanent magnet motors by attention mechanisms and multiscale convolutional residual network," IEEE Trans. Ind. Electron., vol. 71, no. 8, pp. 9737-9746, Aug. 2024.
[16] K. J. Shih, M. F. Hsieh, B. J. Chen, and S. F. Huang, "Machine learning for inter-turn short-circuit fault diagnosis in permanent magnet synchronous motors," IEEE Trans. Magn., vol. 58, no. 8, pp. 1-7, Aug. 2022.
[17] Q. Song, M. Wang, W. Lai, and S. Zhao, "Multiscale kernel-based residual CNN for estimation of inter-turn short circuit fault in PMSM," Sensors, vol. 22, no. 18, Article ID: 6870, Sept. 2, 2022.
[18] W. Sun, A. R. Paiva, P. Xu, A. Sundaram, and R. D. Braatz, "Fault detection and identification using Bayesian recurrent neural networks," Comput. Chem. Eng., vol. 141, Article ID: 106991, Oct. 2020.
[19] P. Pietrzak and M. Wolkiewicz, "Machine learning-based stator current data-driven PMSM stator winding fault diagnosis," Sensors, vol. 22, no. 24, Article ID: 9668, Dec. 2 2022.
[20] Y. Qi, M. Zafarani, V. Gurusamy, and B. Akin, "Advanced severity monitoring of interturn short circuit faults in PMSMs," IEEE Trans. Transp. Electrific., vol. 5, no. 2, pp. 395-404, Jun. 2019.
[21] S. Huang, et al., "Mitigation of interturn short-circuits in IPMSM by using MTPCC control adaptive to fault severity," IEEE Trans. Power Electron., vol. 37, no. 4, pp. 4685-4696, Apr. 2021.
[22] Y. Qi, E. Bostanci, M. Zafarani, and B. Akin, "Severity estimation of interturn short circuit fault for PMSM," IEEE Trans. Ind. Electron., vol. 66, no. 9, pp. 7260-7269, Sept. 2018. [23] C. Fan, M. Chen, X. Wang, J. Wang, and B. Huang, "A review on data preprocessing techniques toward efficient and reliable knowledge discovery from building operational data," Front. Energy Res., vol. 9, Article ID: 652801, Mar. 2021.
[24] D. Divya, B. Marath, and M. B. Santosh Kumar, "Review of fault detection techniques for predictive maintenance," J. Qual. Maint. Eng., vol. 29, no. 2, pp. 420-441, 2023.
[25] S. M. Alessio, "Discrete wavelet transform (DWT)," in Silvia Maria Alessio, Ed., Digital Signal Processing and Spectral Analysis for Scientists: Concepts and Applications, pp. 645-714, 2016.
[26] P. K. Rahi and R. Mehra, "Analysis of power spectrum estimation using Welch method for various window techniques," Int. J. Emerg. Technol. Eng., vol. 2, no. 6, pp. 106-109, 2014.
[27] W. Jung, S. H. Yun, Y. S. Lim, S. Cheong, and Y. H. Park, "Vibration and current dataset of three-phase permanent magnet synchronous motors with stator faults," Data Brief, vol. 47, Article ID: 108952, Apr. 2023.
[28] K. Shen, H. Wang, A. Chaudhuri, and Z. Asgharzadeh, "Automatic Gaussian bandwidth selection for kernel principal component analysis," in Proc. Int. Conf. Knowl. Sci. Eng. Manag., pp. 15-26, Guangzhou, China,16-18 Aug. 2023.
[29] L. Breiman, Classification and Regression Trees, Routledge, 2017.
[30] L. Zou, K. J. Zhuang, and J. Hu, "A Bayesian adaptive resize-residual deep learning network for fault diagnosis of rotating machinery," in Proc. 17th EASEC Conf., pp. 783-801, Singapore, 27-30 2022.
[31] R. M. Neal, Bayesian Learning for Neural Networks, vol. 118, Springer, 2012.
[32] D. J. MacKay, "A practical Bayesian framework for backpropagation networks," Neural Comput., vol. 4, no. 3, pp. 448-472, May 1992.
[33] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, "Dropout: a simple way to prevent neural networks from overfitting," J. Mach. Learn. Res., vol. 15, no. 1, pp. 1929-1958, 2014.
[34] I. Goodfellow, Deep Learning, vol. 196, MIT Press, 2016.
[35] A. Graves, "Practical variational inference for neural networks," Adv. Neural Inf. Process. Syst., vol. 24, 2011.
[36] J. Yan, S. Senemmar, and J. Zhang, "Inter-turn short circuit fault diagnosis and severity estimation for wind turbine generators," in J. Phys.: Conf. Ser., vol. 2767, no. 3, Article ID: 032021, 2024.
[37] L. Breiman, Classification and Regression Trees, Routledge, 2017.
[38] L. Zou, K. J. Zhuang, and J. Hu, "A Bayesian adaptive resize-residual deep learning network for fault diagnosis of rotating machinery," in Proc.17th East Asian-Pacific Conf. Struct. Eng. Constr.,pp. 783-801, Singapore, 27-30 Jun. 2022.
[39] R. M. Neal, Bayesian Learning for Neural Networks, vol. 118, Springer, 2012.
[40] D. J. MacKay, "A practical Bayesian framework for backpropagation networks," Neural Comput., vol. 4, no. 3, pp. 448-472, May 1992.
[41] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, "Dropout: a simple way to prevent neural networks from overfitting," J. Mach. Learn. Res., vol. 15, no. 1, pp. 1929-1958, Jan. 2014.
[42] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016.
[43] A. Graves, "Practical variational inference for neural networks," Adv. Neural Inf. Process. Syst., vol. 24, 2011.
[44] J. Yan, S. Senemmar, and J. Zhang, "Inter-turn short circuit fault diagnosis and severity estimation for wind turbine generators," J. Phys.: Conf. Ser., vol. 2767, no. 3, Article ID: 032021, 2024.
