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Inter-turn short circuit (ITSC) is a frequent fault of interior permanent magnet synchronous motors (IPMSM). If ITSC faults are not promptly monitored, it may result in secondary faults or even cause extensive damage to the entire motor. To enhance the reliability of IPMSMs, this paper introduces a fault diagnosis method specifically designed for identifying ITSC faults in IPMSMs. The sparse coefficients of phase current and torque are solved by clustering shrinkage stage orthogonal matching tracking (CcStOMP) in the greedy tracking algorithm.The CcStOMP algorithm can extract multiple target atoms at one time, which greatly improves the iterative efficiency. The multiple features are utilized as input parameters for constructing the random forest classifier. The constructed random forest model is used to diagnose ITSC faults with the results showing that the random forest model has a diagnostic accuracy of 98.61% using all features, and the diagnostic accuracy of selecting three of the most important features is still as high as 97.91%. The random forest classification model has excellent robustness that maintains high classification accuracy despite the reduction of feature vectors, which is a great advantage compared to other classification algorithms. The combination of greedy tracing and the random forest is not only a fast diagnostic model but also a model with good generalisation and anti-interference capability. This non-invasive method is applicable to monitoring and detecting failures in industrial PMSMs.
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Tom
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art. no. e148943
Opis fizyczny
Bibliogr 24 poz., rys., tab.
Twórcy
autor
- School of Automobile, Chang’an University, Xi’an 710064, China
autor
- School of Automobile, Chang’an University, Xi’an 710064, China
autor
- School of Automobile, Chang’an University, Xi’an 710064, China
autor
- School of Automobile, Chang’an University, Xi’an 710064, China
autor
- School of Automobile, Chang’an University, Xi’an 710064, China
autor
- School of Automobile, Chang’an University, Xi’an 710064, China
autor
- School of Automobile, Chang’an University, Xi’an 710064, China
Bibliografia
- [1] N. Soundirarajan, K. Srinivasan, and A. Baggio, “Lyapunov stability based sliding mode observer for sensorless control of permanent magnet synchronous motor,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 70, no. 2, p. e140353, Apr. 2022, doi: 10.24425/bpasts.2022.140353.
- [2] H. Qiu, Y. Zhang, C. Yang, and R. Yi, “Influence of the number of turns on the performance of permanent magnet synchronous motor,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 68, no. 3, pp. 429–436, Jun. 2020, doi: 10.24425/bpasts.2020.133375.
- [3] P. Pietrzak and M. Wolkiewicz, “Stator winding fault detection of permanent magnet synchronous motors based on the bispectrum analysis,” Bull. Pol. Acad. Sci.-Tech. Sci., vol. 70, no. 2, p. e140556, Apr. 2022, doi: 10.24425/bpasts.2022.140556.
- [4] D. Bochao, C. Shumei, H. Shouliang, W. Guoliang, and X. Bingliang, “A Simple Diagnosis of Winding Short-Circuited Fault of PMSM for Electric Vehicle,” in 2012 IEEE Vehicle Power and Propulsion Conference (VPPC), Korea (South), 2012, pp. 88–91, doi: 10.1109/VPPC.2012.6422577.
- [5] J. Hang, S. Ding, J. Zhang, M. Cheng, W. Chen, and Q. Wang, “Detection of Interturn Short-Circuit Fault for PMSM With Simple Fault Indicator,” IEEE Trans. Energy Convers., vol. 31, no. 4, pp. 1697–1699, Dec. 2016, doi: 10.1109/TEC.2016.2583780.
- [6] Y.-L. He et al., “Impact of Stator Interturn Short Circuit Position on End Winding Vibration in Synchronous Generators,” IEEE Trans. Energy Convers., vol. 36, no. 2, pp. 713–724, Jun. 2021, doi: 10.1109/TEC.2020.3021901.
- [7] J. Hang, J. Zhang, M. Xia, S. Ding, and W. Hua, “Interturn Fault Diagnosis for Model-Predictive-Controlled-PMSM Based on Cost Function and Wavelet Transform,” IEEE Trans. Power Electron., vol. 35, no. 6, pp. 6405–6418, Jun. 2020, doi: 10.1109/TPEL.2019.2953269.
- [8] P.C.M. Lamim Filho, R. Pederiva, and J.N. Brito, “Detection of stator winding faults in induction machines using flux and vibration analysis,” Mech. Syst. Signal Proc., vol. 42, no. 1–2, pp. 377–387, Jan. 2014, doi: 10.1016/j.ymssp.2013.08.033.
- [9] V. Hegde and M.G.S. Rao, “Detection of Stator Winding Inter-Turn Short Circuit Fault in Induction Motor Using Vibration Signals by MEMS Accelerometer,” Electr. Power Compon. Syst., vol. 45, no. 13, pp. 1463–1473, 2017, doi: 10.1080/15325008.2017.1358777.
- [10] S. Huang, A. Aggarwal, E.G. Strangas, B. Khoshoo, K. Li, and F. Niu, “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. 2022, doi: 10.1109/TPEL.2021.3127538.
- [11] R. Cui, Y. Fan, and C. Li, “On-Line Inter-Turn Short-Circuit Fault Diagnosis and Torque Ripple Minimization Control Strategy Based on OW Five-Phase BFTHE-IPM,” IEEE Trans. Energy Convers., vol. 33, no. 4, pp. 2200–2209, Dec. 2018, doi: 10.1109/TEC.2018.2851615.
- [12] H. Liang, Y. Chen, S. Liang, and C. Wang, “Fault Detection of Stator Inter-Turn Short-Circuit in PMSM on Stator Current and Vibration Signal,” Appl. Sci.-Basel, vol. 8, no. 9, p. 1677, Sep. 2018, doi: 10.3390/app8091677.
- [13] Y.-J. Goh and O. Kim, “Linear Method for Diagnosis of Inter-Turn Short Circuits in 3-Phase Induction Motors,” Appl. Sci.-Basel, vol. 9, no. 22, p. 4822, Nov. 2019, doi: 10.3390/app9224822.
- [14] S. Shao, R. Yan, Y. Lu, P. Wang, and R.X. Gao, “DCNN-Based Multi-Signal Induction Motor Fault Diagnosis,” IEEE Trans. Instrum. Meas., vol. 69, no. 6, pp. 2658–2669, Jun. 2020, doi: 10.1109/TIM.2019.2925247.
- [15] 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, doi: 10.1109/ACCESS.2020.3000856.
- [16] B. Yang, R. Liu, and X. Chen, “Fault Diagnosis for a Wind Turbine Generator Bearing via Sparse Representation and Shift-Invariant K-SVD,” IEEE Trans. Ind. Inform., vol. 13, no. 3, pp. 1321–1331, Jun. 2017, doi: 10.1109/TII.2017.2662215.
- [17] Z. Zhang, Y. Xu, J. Yang, X. Li, and D. Zhang, “A Survey of Sparse Representation: Algorithms and Applications,” IEEE Access, vol. 3, pp. 490–530, 2015, doi: 10.1109/ACCESS.2015.2430359.
- [18] H. Zhang, X. Chen, Z. Du, and R. Yan, “Kurtosis based weighted sparse model with convex optimization technique for bearing fault diagnosis,” Mech. Syst. Signal Proc., vol. 80, pp. 349–376, Dec. 2016, doi: 10.1016/j.ymssp.2016.04.033.
- [19] W. He, Y. Ding, Y. Zi, and I.W. Selesnick, “Sparsity-based algorithm for detecting faults in rotating machines,” Mech. Syst. Signal Proc., vol. 72–73, pp. 46–64, May 2016, doi: 10.1016/j.ymssp.2015.11.027.
- [20] D.L. Donoho, Y. Tsaig, I. Drori, and J.-L. Starck, “Sparse Solution of Underdetermined Systems of Linear Equations by Stagewise Orthogonal Matching Pursuit,” IEEE Trans. Inf. Theory, vol. 58, no. 2, pp. 1094–1121, Feb. 2012, doi: 10.1109/TIT.2011.2173241.
- [21] L. Song and R. Yan, “Bearing fault diagnosis based on Cluster-contraction Stage-wise Orthogonal-Matching-Pursuit,” Measurement, vol. 140, pp. 240–253, Jul. 2019, doi: 10.1016/j.measurement.2019.03.061.
- [22] J. Fang, Y. Sun, Y. Wang, B. Wei, and J. Hang, “Improved ZSVC-based fault detection technique for incipient stage inter-turn fault in PMSM,” IET Electr. Power Appl., vol. 13, no. 12, pp. 2015–2026, Dec. 2019, doi: 10.1049/iet-epa.2019.0016.
- [23] S. Liang, Y. Chen, H. Liang, and X. Li, “Sparse Representation and SVM Diagnosis Method for Inter-Turn Short-Circuit Fault in PMSM,” Appl. Sci.-Basel, vol. 9, no. 2, p. 224, Jan. 2019, doi: 10.3390/app9020224.
- [24] J.C. Quiroz, N. Mariun, M.R. Mehrjou, M. Izadi, N. Misron, and M.A.M. Radzi, “Fault detection of broken rotor bar in LS-PMSM using random forests,” Measurement, vol. 116, pp. 273–280, Feb. 2018, doi: 10.1016/j.measurement.2017.11.004.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-4c57a9f5-a466-4d3d-a911-43eb9256e7bd