Semi-supervised machine learning for motor eccentricity fault diagnosis
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Published
Sep 4, 2023
Bingnan Wang
Shen Zhang
Hiroshi Inoue
Makoto Kanemaru
Abstract
Eccentricity is one major indicator of mechanical faults in electric machines and needs to be detected early to avoid machine failures. Data-driven techniques based on machine learning and deep learning algorithms have been proposed in recent years for motor fault detection. However, majority of these methods use supervised learning algorithms and require large, labelled datasets, which can be challenging to obtain. In this paper, we propose a semi-supervised learning method based on a deep generative model using variational auto-encoder for eccentricity fault quantification. Good prediction accuracy can be achieved when only a small subset of training data has labels.
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Keywords
fault detection, eccentricity, semi-supervised learning
References
Akar, M. (2013). Detection of a static eccentricity fault in a closed loop driven induction motor by using the angular domain order tracking analysis method. Mechanical Systems and Signal Processing, 34(1-2), 173–182.
Benbouzid, M. E. H. (2000). A review of induction motors signature analysis as a medium for faults detection. IEEE Transactions on Industrial Electronics, 47(5), 984–993.
Chen, X., Wang, Z., Zhang, Z., Jia, L., & Qin, Y. (2018). A semi-supervised approach to bearing fault diagnosis under variable conditions towards imbalanced unlabeled data. Sensors, 18(7), 2097.
Harmouche, J., Delpha, C., & Diallo, D. (2015, March). Improved fault diagnosis of ball bearings based on the global spectrum of vibration signals. IEEE Transactions on Energy Conversion, 30(1), 376-383.
Kang, M., Kim, J., & Kim, J. (2015, April). An FPGA-based multicore system for real-time bearing fault diagnosis using ultrasampling rate AE signals. IEEE Transactions on Industrial Electronics, 62(4), 2319-2329.
Kingma, D. P., Mohamed, S., Rezende, D. J., & Welling, M. (2014). Semi-supervised learning with deep generative models. In Neurips (pp. 3581–3589).
Liu, C., & Gryllias, K. (2020). A semi-supervised support vector data description-based fault detection method for rolling element bearings based on cyclic spectral analysis. Mech. Syst. Signal Process., 140, 106682.
Liu, H., Zhou, J., Xu, Y., Zheng, Y., Peng, X., & Jiang, W. (2018). Unsupervised fault diagnosis of rolling bearings using a deep neural network based on generative adversarial networks. Neurocomput., 315, 412–424.
Nandi, S., Ahmed, S., & Toliyat, H. A. (2001). Detection of rotor slot and other eccentricity related harmonics in a three phase induction motor with different rotor cages. IEEE Transactions on Energy Conversion, 16(3), 253– 260.
Nandi, S., Toliyat, H. A., & Li, X. (2005). Condition monitoring and fault diagnosis of electrical motors—a review. IEEE Transactions on Energy Conversion, 20(4), 719– 729.
R-Far, R., Hallaji, E., F-Zanjani, M., Saif, M., Kia, S. H., Henao, H., & Capolino, G. (2019, Aug). Information fusion and semi-supervised deep learning scheme for diagnosing gear faults in induction machine systems. IEEE Transactions on Industrial Electronics, 66(8), 6331-6342.
Verstraete, D. B., Droguett, E. L., Meruane, V., Modarres, M., & Ferrada, A. (2019). Deep semi-supervised generative adversarial fault diagnostics of rolling element bearings. Structural Health Monitoring, 1-22.
Wang, B., Albader, M. W., Inoue, H., & Kanemaru, M. (2022). Induction motor eccentricity fault analysis and quantification with modified winding function based model. In 2022 25th International Conference on Electrical Machines and Systems (ICEMS) (p. 1-6). doi: 10.1109/ICEMS56177.2022.9983377
Wen, L., Li, X., Gao, L., & Zhang, Y. (2018). A new convolutional neural network-based data-driven fault diagnosis method. IEEE Transactions on Industrial Electronics, 65(7), 5990-5998.
Zhang, S., Ye, F., Wang, B., & Habetler, T. G. (2021). Semi-supervised bearing fault diagnosis and classification using variational autoencoder-based deep generative models. IEEE Sensors Journal, 21(5), 6476-6486. doi: 10.1109/JSEN.2020.3040696
Zhang, S., Zhang, S., Wang, B., & Habetler, T. G. (2020). Deep learning algorithms for bearing fault diagnostics—a comprehensive review. IEEE Access, 8, 29857– 29881.
Zhou, L., Wang, B., Lin, C., Inoue, H., & Miyoshi, M. (2021). Static eccentricity fault detection for psh-type induction motors considering high-order air gap permeance harmonics. In 2021 IEEE International Electric Machines & Drives Conference (IEMDC) (p. 1-7). doi: 10.1109/IEMDC47953.2021.9449496
Benbouzid, M. E. H. (2000). A review of induction motors signature analysis as a medium for faults detection. IEEE Transactions on Industrial Electronics, 47(5), 984–993.
Chen, X., Wang, Z., Zhang, Z., Jia, L., & Qin, Y. (2018). A semi-supervised approach to bearing fault diagnosis under variable conditions towards imbalanced unlabeled data. Sensors, 18(7), 2097.
Harmouche, J., Delpha, C., & Diallo, D. (2015, March). Improved fault diagnosis of ball bearings based on the global spectrum of vibration signals. IEEE Transactions on Energy Conversion, 30(1), 376-383.
Kang, M., Kim, J., & Kim, J. (2015, April). An FPGA-based multicore system for real-time bearing fault diagnosis using ultrasampling rate AE signals. IEEE Transactions on Industrial Electronics, 62(4), 2319-2329.
Kingma, D. P., Mohamed, S., Rezende, D. J., & Welling, M. (2014). Semi-supervised learning with deep generative models. In Neurips (pp. 3581–3589).
Liu, C., & Gryllias, K. (2020). A semi-supervised support vector data description-based fault detection method for rolling element bearings based on cyclic spectral analysis. Mech. Syst. Signal Process., 140, 106682.
Liu, H., Zhou, J., Xu, Y., Zheng, Y., Peng, X., & Jiang, W. (2018). Unsupervised fault diagnosis of rolling bearings using a deep neural network based on generative adversarial networks. Neurocomput., 315, 412–424.
Nandi, S., Ahmed, S., & Toliyat, H. A. (2001). Detection of rotor slot and other eccentricity related harmonics in a three phase induction motor with different rotor cages. IEEE Transactions on Energy Conversion, 16(3), 253– 260.
Nandi, S., Toliyat, H. A., & Li, X. (2005). Condition monitoring and fault diagnosis of electrical motors—a review. IEEE Transactions on Energy Conversion, 20(4), 719– 729.
R-Far, R., Hallaji, E., F-Zanjani, M., Saif, M., Kia, S. H., Henao, H., & Capolino, G. (2019, Aug). Information fusion and semi-supervised deep learning scheme for diagnosing gear faults in induction machine systems. IEEE Transactions on Industrial Electronics, 66(8), 6331-6342.
Verstraete, D. B., Droguett, E. L., Meruane, V., Modarres, M., & Ferrada, A. (2019). Deep semi-supervised generative adversarial fault diagnostics of rolling element bearings. Structural Health Monitoring, 1-22.
Wang, B., Albader, M. W., Inoue, H., & Kanemaru, M. (2022). Induction motor eccentricity fault analysis and quantification with modified winding function based model. In 2022 25th International Conference on Electrical Machines and Systems (ICEMS) (p. 1-6). doi: 10.1109/ICEMS56177.2022.9983377
Wen, L., Li, X., Gao, L., & Zhang, Y. (2018). A new convolutional neural network-based data-driven fault diagnosis method. IEEE Transactions on Industrial Electronics, 65(7), 5990-5998.
Zhang, S., Ye, F., Wang, B., & Habetler, T. G. (2021). Semi-supervised bearing fault diagnosis and classification using variational autoencoder-based deep generative models. IEEE Sensors Journal, 21(5), 6476-6486. doi: 10.1109/JSEN.2020.3040696
Zhang, S., Zhang, S., Wang, B., & Habetler, T. G. (2020). Deep learning algorithms for bearing fault diagnostics—a comprehensive review. IEEE Access, 8, 29857– 29881.
Zhou, L., Wang, B., Lin, C., Inoue, H., & Miyoshi, M. (2021). Static eccentricity fault detection for psh-type induction motors considering high-order air gap permeance harmonics. In 2021 IEEE International Electric Machines & Drives Conference (IEMDC) (p. 1-7). doi: 10.1109/IEMDC47953.2021.9449496
Section
Special Session Papers
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