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Diagnostic System for Induction Motor Stator Winding Faults Based on Axial Flux

effective neural approach for the automatic location of stator interturn faults in induction motor , IEEE Trans. Ind. El., 2008, 55, (12), 4277–4289. [7] W olkiewicz M., K owalski C.T., On-line neural network-based stator fault diagnosis system of the converter-fed induction motor drive , 39th Annual Conference of the IEEE Industrial Electronics Society, Vienna, Austria, 2013, 5561–5566. [8] C ruz S.M.A., C ardoso A.J.M., Stator winding fault diagnosis in three-phase synchronous and asynchronous motors, by the extended Park’s vector approach , IEEE Trans

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Fault Diagnosis of Low-Power Three-Phase Induction Motor

References 1. Cardoso, A.J.M. & Saraiva, E.S. (1993). Computer-aided detection of airgap eccentricity in operating three-phase induction motor by Park’s vector approach. IEEE Transactions on Industry Applications . 29(5), 897–901. DOI: 10.1109/28.245712. 2. Cardoso, A.J.M., Cruz, S.M.A. & Fonseca, D.S.B. (1999). Inter-turn stator winding fault diagnosis in three-phase induction motors by Park’s vector approach. IEEE Transactions on Energy Conversion . 14(3), 595–598. DOI: 10.1109/60.790920. 3. Estima, J.O., Freire, N.M.A., & Cardoso, A

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Use of Support Vector Machines for Classification of Defects in the Induction Motor

. DOI: 10.1017/CBO9780511801389.012 [13] Hadef, M., Djerdir, A., Ikhlef N., Mekideche, M. R., and N’diaye, A. O., “A Fault severity index for stator winding faults detection in vector controlled PM synchronous motor”, J. Electr Eng Techno , vol 10 (6), pp.2326-2333 2015. [14] Namdarin, M., and Jazayeri-Rad, H., “Incipient fault diagnosis using support vector machines based on monitoring continuous decision functions”, Engineering Applications of Artificial Intelligence , vol 28, 2014, pp 22-35. [15] Weston, J., and Watkings, C., “Support vector

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Application of Neural Networks and Axial Flux for the Detection of Stator and Rotor Faults of an Induction Motor

Genetic Algorithm Based Techniques . Oxford: Oxford University Press. Wolkiewicz, M. and Kowalski, C. T. (2016). Incipient stator fault detector based on neural networks and symmetrical components analysis for induction motor drives. In: 2016 13th Selected Issues of Electrical Engineering and Electronics (WZEE) , IEEE, Rzeszow, Poland, 4–8 May 2016, pp. 1–7. Wolkiewicz, M. and Skowron, M. (2017). Diagnostic system for induction motor stator winding faults based on axial flux. Power Electronics and Drives , 2(37)(2), pp. 137–150. Wolkiewicz, M

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Improving Legibility of Motor Current Spectrum for Broken Rotor Bars Fault Diagnostics

. B Electr. Power Appl. , vol. 133, no. 3, p. 155, 1986. [18] R. R. Schoen and T. G. Habetler, “Effects of time-varying loads on rotor fault detection in induction machines,” IEEE Trans. Ind. Appl. , vol. 31, no. 4, pp. 900–906, 1995. [19] H. Henao, C. Demian, and G.-A. Capolino, “A frequency-domain detection of stator winding faults in induction machines using an external flux sensor,” IEEE Trans. Ind. Appl. , vol. 39, no. 5, pp. 1272–1279, Sep. 2003.

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A Survey of Broken Rotor Bar Fault Diagnostic Methods of Induction Motor

,” in 2017 IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) , 2017, pp. 427–433. [33] S. M. A. Cruz, A. J. Marques Cardoso, “Rotor Cage Fault Diagnosis in Three-Phase Induction Motors by Extended Park’s Vector Approach,” Electr. Mach. Power Syst ., vol. 28, no. 4, pp. 289–299, Apr. 2000. [34] S. M. A. Cruz and A. J. M. Cardoso, “Stator Winding Fault Diagnosis in Three-Phase Synchronous and Asynchronous

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