Squirrel Cage Induction Motor Defects Diagnosis Using Lissajous Curve of an Auxiliary Winding Voltage
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Developing a non-invasive condition monitoring method for a squirrel cage induction machine and its fault diagnosis is the objective of this paper. Several signals can be used to monitor the Induction Machines (IMs) such as power input, stator voltage, and stator current. In this research a new processing signal is developed as an auxiliary winding voltage. The auxiliary winding is considered as a small coil inserting between the stator phases. This approach is based on a Lissajous curve of the auxiliary winding voltage Park component. First, the squirrel cage induction motor is mathematically modeled by considering the real geometry of the rotor structure. After that, the auxiliary winding voltage expression and its Park component are presented. In order to verify the proposed approach effectiveness, the simulation has been carried out in MATLAB for non-defected motor and motor with different load level. The results have confirmed the capability of this method to detect failures.
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Gentile, G., Meo, S., Ometto, A., Induction motor current signature analysis to diagnostics, of stator short circuits, (2003) IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, SDEMPED 2003 - Proceedings, art. no. 1234545, pp. 47-51.
J. Y. Khadouj and E. M. Lamiaà, Electromechanical Conversion Chain Fault Diagnostic - State of Art, ATS-2018 Proceeding of Engineering and Technology ,vol. 36, pp. 1-5, 2018.
Harir, M., Bendiabdellah, A., Induction Motor Faults Diagnosis when Considering Bars Skew and Saturation Effects, (2019) International Journal on Energy Conversion (IRECON), 7 (1), pp. 18-28.
Bindu, S., Thomas, V., A Modified Direct-Quadrature Axis Model for Characterization of Air-gap Mixed Eccentricity Faults in Three-Phase Induction Motor, (2018) International Review on Modelling and Simulations (IREMOS), 11 (6), pp. 359-365.
J. P. Amezquita-Sanchez, M. Valtierra-Rodriguez, C. A. Perez-Ramirez, D. Camarena-Martinez, A. Garcia-Perez, and R. J. Romero-Troncoso, Fractal dimension and fuzzy logic systems for broken rotor bar detection in induction motors at start-up and steady-state regimes, Meas. Sci. Technol., vol. 28, no. 7, 2017.
M. Riera-Guasp, J. A. Antonino-Daviu, and G. A. Capolino, Advances in electrical machine, power electronic, and drive condition monitoring and fault detection: State of the art, IEEE Trans. Ind. Electron., vol. 62, no. 3, pp. 1746-1759, 2015.
W. Laala, S. E. Zouzou, and S. Guedidi, Induction motor broken rotor bars detection using fuzzy logic: Experimental research, Int. J. Syst. Assur. Eng. Manag., vol. 5, no. 3, pp. 329-336, 2014.
Y. Han and Y. H. Song, Condition Monitoring Techniques for Electrical Equipment - A Literature Survey, IEEE Transactions On Power Delivery vol. 18, no. 1, pp. 4-13, 2003.
W.T. Thomson, M. Fenger, Current signature analysis to detect induction motor faults, IEEE Industry Applications Magazine, july/August 2001, pp. 26-34.
M. Roman, Analysis of Squirrel Cage Motor with Broken Bars and Rings, Transactions on Electrical Engineering, vol. 1, no. 2, pp. 36-42, 2012.
Damiano, A., Gatto, G., Marongiu, I., Meo, S., Perfetto, A., Serpi, A., A predictive direct torque control of induction machines, (2012) International Review of Electrical Engineering (IREE), 7 (4), pp. 4837-4844.
A. Yazidi, H. Henao, G. A Capolino, Broken Rotor Bars Fault Detection in Squirrel Cage Induction Machines, IEEE International Conference on Electric Machines and Drives, 2005. no. 9054247. pp. 741-747, 2005.
Y. Liu, L. Guo, Q. Wang, G. An, M. Guo, and H. Lian, Application to induction motor faults diagnosis of the amplitude recovery method combined with FFT, Mech. Syst. Signal Process., vol. 24, no. 8, pp. 2961-2971, 2010.
M. P. Sanchez and J. a Antonino-daviu, J. R. Folch J. P. Cruz and R. P. Panadero Diagnosis of Induction Motor Faults in the Fractional Fourier Domain IEEE Transactions On Instrumentation And Measurement, vol. 59, no. 8, pp.1-11, august 2010.
B. Saddam, B. S. Ahmed, A. Aissa, T. Ali, T. De Laghouat, and B. P. Laghouat, Squirrel Cage Induction Motor under Stator and Rotor Bars Faults Modeling and Diagnosis, 2018 Int. Conf. Commun. Electr. Eng., pp. 1-6.
P. K. Kankar, S. C. Sharma, and S. P. Harsha, Rolling element bearing fault diagnosis using autocorrelation and continuous wavelet transform, JVC/Journal Vib. Control, vol. 17, no. 14, pp. 2081-2094, 2011.
O. Duque-Perez, L. A. Garcia-Escudero, D. Morinigo-Sotelo, P. E. Gardel, and M. Perez-Alonso, Analysis of fault signatures for the diagnosis of induction motors fed by voltage source inverters using ANOVA and additive models, Electr. Power Syst. Res., vol. 121, pp. 1-13, 2015.
A. Naha, A. K. Samanta, A. Routray, and A. K. Deb, A method for detecting half-broken rotor bar in lightly loaded induction motors using current, IEEE Trans. Instrum. Meas., vol. 65, no. 7, pp. 1614-1625, 2016.
A. Bouzida, O. Touhami, R. Ibtiouen, A. Belouchrani, M. Fadel, and A. Rezzoug, Fault diagnosis in industrial induction machines through discrete wavelet transform, IEEE Trans. Ind. Electron., vol. 58, no. 9, pp. 4385-4395, 2011.
D. A. Asfani, A. K. Muhammad, Syafaruddin, M. H. Purnomo, and T. Hiyama, Temporary short circuit detection in induction motor winding using combination of wavelet transform and neural network, Expert Syst. Appl., vol. 39, no. 5, pp. 5367-5375, 2012.
V. N. Ghate and S. V. Dudul, Cascade neural-network-based fault classifier for three-phase induction motor, IEEE Trans. Ind. Electron., vol. 58, no. 5, pp. 1555-1563, 2011.
V. P. Mini and S. Ushakumari, Electrical fault detection and diagnosis of induction motor using fuzzy Logic, Adv. Model. Anal. B, vol. 55, no. 1-2, pp. 22-40, 2012.
H. Razik, M. B. de Rossiter Corrêa, and E. R. C. da Silva, A novel monitoring of load level and broken bar fault severity applied to squirrel-cage induction motors using a genetic algorithm, IEEE Trans. Ind. Electron., vol. 56, no. 11, pp. 4615-4626, 2009.
A. Saghafinia, S. Kahourzade, A. Mahmoudi, W. P. Hew, and M. N. Uddin, On line trained fuzzy logic and adaptive continuous wavelet transform based high precision fault detection of IM with broken rotor bars, Conf. Rec. - IAS Annu. Meet. (IEEE Ind. Appl. Soc., pp. 1-8, 2012.
Tran, C., Brandstetter, P., Kuchar, M., Ho, S., A Novel Speed and Current Sensor Fault-Tolerant Control Based on Estimated Stator Currents in Induction Motor Drives, (2020) International Review of Electrical Engineering (IREE), 15 (5), pp. 344-351.
L. El Menzhi and A. Saad, Induction motor fault diagnosis using voltage spectrum of auxiliary winding and lissajous curve of its park components, Adv. Mater. Res., vol. 805-806, pp. 963-979, 2013.
H. A. H. Al-Khazali, Geometrical and Graphical Representations Analysis of Lissajous Figures in Rotor Dynamic System, IOSR J. Eng., vol. 02, no. 05, pp. 971-978, 2012.
Y. K. Jelbaoui, E. M. Lamiaà, and A. Saad, Fault diagnosis of a squirrel cage induction motor fed by an inverter using lissajous curve of an auxiliary winding voltage, Indones. J. Electr. Eng. Comput. Sci., vol. 21, no. 3, pp. 1299-1308, 2021.
Soufi, Y., Bahi, T., Harkat, M., Mohammedi, M., Fault Diagnosis Methods for Three Phase PWM Inverter Fed Induction Motor, (2018) International Journal on Engineering Applications (IREA), 6 (4), pp. 122-127.
A. Ibrahim and M. Marei, Modeling of induction motor based on winding function theory to study motor under stator/rotor internal faults, IEEE proceedings, MEPCON, no. 1, pp. 494-500, December 2010,
G. Didier, Modeling And Diagnosis Of The Asynchronous Machine In The Presence Of Faults, 2004.
P. Shi, Z. Chen, Y. Vagapov, and Z. Zouaoui, A new diagnosis of broken rotor bar fault extent in three phase squirrel cage induction motor, Mech. Syst. Signal Process., vol. 42, no. 1-2, pp. 388-403, 2014.
Meo, S., Ometto, A., Rotondale, N. Diagnostic-oriented modelling of induction machines with stator short circuits, (2012) International Review on Modelling and Simulations (IREMOS), 5 (3), pp. 1202-1209.
M. Sahraoui and A. Ghoggal, Modelling and Detection of Inter-Turn Short Circuits in Stator Windings of Induction Motor, IECON 2006 - 32nd Annual Conf on IEEE Ind Electrs, pp. 4981-4986.
I. Ouachtouk, S. El Hani, S. Guedira, K. Dahi, and H. Mediouni, Broken rotor bar fault detection based on stator current envelopes analysis in squirrel cage induction machine, 2017 IEEE Int. Electr. Mach. Drives Conf. IEMDC 2017, pp. 2-7, 2017.
G. Bossio, C. De Angelo, J. Solsona, G. Garcia, and M. I. Valla, A 2-D model of the induction machine: An extension of the modified winding function approach, IEEE Trans. Energy Convers., vol. 19, no. 1, pp. 144-150, 2004.
M. Ojaghi, M. Sabouria, J. Faizb, and V. Ghorbanianb, Exact modeling and simulation of saturated induction motors with broken rotor bars fault using winding function approach, Int. J. Eng. Trans. A Basics, vol. 27, no. 1, pp. 69-78, 2014.
M. Boucherma, M. Y. Kaikaa, and A. Khezzar Park model of squirrel cage induction machine including space harmonics effects Journal of Electrical Engineering ,vol. 57, no. 4, pp. 193-199, 2006.
L. El Menzhi and A. Saad, Three phase induction motor inverter defects diagnosis using voltage spectrum of an auxiliary winding, Appl. Mech. Mater., vol. 672-674El, pp. 1244-1252, 2014.
L. El Menzhi and A. Saad, Lissajous curve of an auxiliary winding voltage park components for diagnosing multiple open switches faults in three phase inverter, Appl. Mech. Mater., vol. 672-674, pp. 1224-1233, 2014.
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