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Condition Monitoring and Fault Diagnosis of Permanent Magnet Synchronous Motor Stator Winding Using the Continuous Wavelet Transform and Machine Learning

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Bhuiyan, E.A., Akhand, M.A., Das, S.L., Ali, F., Tasneem, Z., Island, R., Saha, D.K., Badal, F.R., Ahamed, H. Moyeen, S.I. (2020). A Survey on Fault Diagnosis and Fault Tolerant Methodologies for Permanent Magnet Synchronous Machines. International Journal of Automation and Computing, 17(6), pp. 763–787. doi: 10.1007/s11633-020-1250-3. Search in Google Scholar

Chen, Y., Liang, S., Li, W., Liang, H. and Wang, C. (2019). Faults and Diagnosis Methods of Permanent Magnet Synchronous Motors: A Review. Applied Sciences, 9(10), p. 2116. doi: 10.3390/app9102116. Search in Google Scholar

Chiddarwar, S. S. and Ramesh Babu, N. (2010). Comparison of RBF and MLP Neural Networks to Solve Inverse Kinematic Problem for 6R Serial Robot by a Fusion Approach. Engineering Applications of Artificial Intelligence, 23(7), pp. 1083–1092. doi: 10.1016/j.engappai.2010.01.028. Search in Google Scholar

Cohen, L. (1989). Time-Frequency Distributions – A Review. Proceedings of the IEEE, 77(7), pp. 941–981. doi: 10.1109/5.30749. Search in Google Scholar

Dai, Y., Ni, S., Xu, D., Zhang, L. and Yan, X.-G. (2021). Disturbance-Observer based Prescribed-Performance Fuzzy Sliding Mode Control for PMSM in Electric Vehicles. Engineering Applications of Artificial Intelligence, 104, p. 104361. doi: 10.1016/j.engappai.2021.104361. Search in Google Scholar

Diao, N., Wang, Z., Ma, H. and Yang, W. (2022). Fault Diagnosis of Rolling Bearing under Variable Working Conditions Based on CWT and T-ResNet. Journal of Vibration Engineering & Technologies, 11, pp. 3747–3757. doi: 10.1007/s42417-022-00780-w. Search in Google Scholar

Ewert, P., Orlowska-Kowalska, T. and Jankowska, K. (2021). Effectiveness Analysis of PMSM Motor Rolling Bearing Fault Detectors Based on Vibration Analysis and Shallow Neural Networks. Energies, 14(3), p. 712. doi: 10.3390/en14030712. Search in Google Scholar

Gao, Y., Li, M., Huang Z. and Lu, J. (2009). A symbol rate estimation algorithm based on Morlet wavelet transform and autocorrelation. In: 2009 IEEE Youth Conference on Information, Computing and Telecommunication. IEEE, pp. 239–242. doi: 10.1109/YCICT.2009.5382378. Search in Google Scholar

García-Cuesta, E., Galván, I. M. and de Castro, A. J. (2008). Multilayer Perceptron as Inverse Model in a Ground-Based Remote Sensing Temperature Retrieval Problem. Engineering Applications of Artificial Intelligence, 21(1), pp. 26–34. doi: 10.1016/j.engappai.2007.03.005. Search in Google Scholar

Haddad, R. Z. and Strangas, E. G. (2016). On the Accuracy of Fault Detection and Separation in Permanent Magnet Synchronous Machines Using MCSA/MVSA and LDA. IEEE Transactions on Energy Conversion, 31(3), pp. 924–934. doi: 10.1109/TEC.2016.2558183. Search in Google Scholar

He, J., Somogyi, C., Strandt, A. and Demerdash, N.A.O. (2014). Diagnosis of stator winding short-circuit faults in an interior permanent magnet synchronous machine. In: 2014 IEEE Energy Conversion Congress and Exposition (ECCE). IEEE, pp. 3125–3130. doi: 10.1109/ECCE.2014.6953825. Search in Google Scholar

Iravani, M. R. and Karimi-Ghartemani, M. (2003). Online Estimation of Steady State and Instantaneous Symmetrical Components. IEEE Proceedings – Generation, Transmission and Distribution, 150(5), p. 616. doi: 10.1049/ip-gtd:20030779. Search in Google Scholar

Kaminski, M. and Orlowska-Kowalska, T. (2015). Adaptive Neural Speed Controllers Applied for A Drive System with An Elastic Mechanical Coupling – A Comparative Study. Engineering Applications of Artificial Intelligence, 45, pp. 152–167. doi: 10.1016/j.engappai.2015.06.011. Search in Google Scholar

Kim, K.-H. (2011). Simple Online Fault Detecting Scheme for Short-Circuited Turn in a PMSM through Current Harmonic Monitoring. IEEE Transactions on Industrial Electronics, 58(6), pp. 2565–2568. doi: 10.1109/TIE.2010.2060463. Search in Google Scholar

Krzysztofiak, M., Skowron, M. and Orlowska-Kowalska, T. (2020). Analysis of the Impact of Stator Inter-Turn Short Circuits on PMSM Drive with Scalar and Vector Control. Energies, 14(1), p. 153. doi: 10.3390/en14010153. Search in Google Scholar

Lilly, J. M. and Olhede, S. C. (2009). Higher-Order Properties of Analytic Wavelets. IEEE Transactions on Signal Processing, 57(1), pp. 146–160. doi: 10.1109/TSP.2008.2007607. Search in Google Scholar

Lilly, J. M. and Olhede, S. C. (2012). Generalized Morse Wavelets as a Superfamily of Analytic Wavelets. IEEE Transactions on Signal Processing, 60(11), pp. 6036–6041. doi: 10.1109/TSP.2012.2210890. Search in Google Scholar

Ma, C. and Chi, Y. (2022). KNN Normalized Optimization and Platform Tuning Based on Hadoop. IEEE Access, 10, pp. 81406–81433. doi: 10.1109/ACCESS.2022.3195872. Search in Google Scholar

Namdari, M. and Jazayeri-Rad, H. (2014). Incipient Fault Diagnosis Using Support Vector Machines based on Monitoring Continuous Decision Functions. Engineering Applications of Artificial Intelligence, 28, pp. 22–35. doi: 10.1016/j.engappai.2013.11.013. Search in Google Scholar

O’Donnell, P. (1985). Report of Large Motor Reliability Survey of Industrial and Commercial Installations, Part I. IEEE Transactions on Industry Applications, 21(4), pp. 853–864. doi: 10.1109/TIA.1985.349532. Search in Google Scholar

Orlowska-Kowalska, T., Wolkiewicz, M., Pietrzak, P., Skowron, M. and Ewe, P. (2022). Fault Diagnosis and Fault-Tolerant Control of PMSM Drives–State of the Art and Future Challenges. IEEE Access, 10, pp. 59979–60024. doi: 10.1109/ACCESS.2022.3180153. Search in Google Scholar

Pietrzak, P., Wolkiewicz, M. and Orlowska-Kowalska, T. (2022). PMSM Stator Winding Fault Detection and Classification Based on Bispectrum Analysis and Convolutional Neural Network. IEEE Transactions on Industrial Electronics, 70, pp. 1–11. doi: 10.1109/TIE.2022.3189076. Search in Google Scholar

Pietrzak, P. and Wolkiewicz, M. (2022). Stator phase current STFT analysis for the PMSM stator winding fault diagnosis. In: 2022 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM). IEEE, pp. 808–813. doi: 10.1109/SPEEDAM53979.2022.9841990 Search in Google Scholar

Shao, S., Yan, R., Lu, Y., Wang, P. and Gao, R. X. (2020). DCNN-Based Multi-Signal Induction Motor Fault Diagnosis. IEEE Transactions on Instrumentation and Measurement, 69(6), pp. 2658–2669. doi: 10.1109/TIM.2019.2925247. Search in Google Scholar

Skowron, M., Krzysztofiak, M. and Orlowska-Kowalska, T. (2022a). Effectiveness of Neural Fault Detectors of Permanent Magnet Synchronous Motor Trained With Symptoms From Field-Circuit Modeling. IEEE Access, 10, pp. 104598–104611. doi: 10.1109/ACCESS.2022.3211087. Search in Google Scholar

Skowron, M., Orlowska-Kowalska, T. and Kowalski, C. T. (2022b). Detection of Permanent Magnet Damage of PMSM Drive Based on Direct Analysis of the Stator Phase Currents Using Convolutional Neural Network. IEEE Transactions on Industrial Electronics, 69(12), pp. 13665–13675. doi: 10.1109/TIE.2022.3146557. Search in Google Scholar

Song, Q., Wang, M., Lai, W. and Zhao, S. (2023). On Bayesian Optimization-Based Residual CNN for Estimation of Inter-Turn Short Circuit Fault in PMSM. IEEE Transactions on Power Electronics, 38(2), pp. 2456–2468. doi: 10.1109/TPEL.2022.3207181. Search in Google Scholar

Urresty, J., Riba, J., Romeral, L., Rosero, J. and Serna, J. (2009). Stator short circuits detection in PMSM by means of Hilbert-Huang transform and energy calculation. In: 2009 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives. IEEE, pp. 1–7. doi: 10.1109/DEMPED.2009.5292789 Search in Google Scholar

Vapnik, V. N. (2000). The Nature of Statistical Learning Theory. New York, NY: Springer New York. Search in Google Scholar

Widodo, A. and Yang, B.-S. (2007). Support Vector Machine in Machine Condition Monitoring and Fault Diagnosis. Mechanical Systems and Signal Processing, 21(6), pp. 2560–2574. doi: 10.1016/j.ymssp.2006.12.007. Search in Google Scholar

Wolkiewicz, M., Tarchala, G., Orlowska-Kowalska, T. and Kowalski, C. T. (2016). Online Stator Interturn Short Circuits Monitoring in the DFOC Induction-Motor Drive. IEEE Transactions on Industrial Electronics, 63(4), pp. 2517–2528. doi: 10.1109/TIE.2016.2520902. Search in Google Scholar

Zaman, S. M. K., Marma, H. U. M. and Liang, X. (2019). Broken rotor bar fault diagnosis for induction motors using power spectral density and complex continuous wavelet transform methods. In: 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE). IEEE, pp. 1–4. doi:10.1109/CCECE.2019.8861517. Search in Google Scholar

Zanardelli, W. G., Strangas, E. G. and Aviyente, S. (2007). Identification of Intermittent Electrical and Mechanical Faults in Permanent-Magnet AC Drives Based on Time–Frequency Analysis. IEEE Transactions on Industry Applications, 43(4), pp. 971–980. doi: 10.1109/TIA.2007.900446. Search in Google Scholar

Zhou, J., Qiu, Y., Zhu, S., Armaghani, D. J., Li, C., Nguyen, H. and Yagiz, S. (2021a). Optimization of Support Vector Machine through the Use of Metaheuristic Algorithms in Forecasting TBM Advance Rate. Engineering Applications of Artificial Intelligence, 97, p. 104015. doi: 10.1016/j.engappai.2020.104015. Search in Google Scholar

Zhou, X., Sun, J., Cui, P., Lu, Y., Lu, M. and Yu, Y. (2021b). A Fast and Robust Open-Switch Fault Diagnosis Method for Variable-Speed PMSM System. IEEE Transactions on Power Electronics, 36(3), pp. 2598–2610. doi: 10.1109/TPEL.2020.3013628. Search in Google Scholar

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