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Differential evolution applied to line-connected induction motors stator fault identification

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Abstract

The three-phase induction motor is the main machine used for electromechanical energy conversion, due to its consolidated construction characteristics. As a consequence of its great importance and industrial application, researches in the fault identification area are constantly conducted to reduce the maintenance rate and the losses, during the productive process, caused by undesirable downtime. In this sense, this work proposes an alternative methodology, based on the differential evolution algorithm, to identify stator short-circuit fault in induction motors connected directly to the electrical grid, using voltage and current signals in time domain. The differential evolution algorithm is used to estimate the electrical parameters of the induction motor, based on the model of the equivalent electrical circuit. Stator fault is identified by calculating the variation of the estimated magnetizing inductance of the motor under no fault condition. The proposed method is validated through experimental tests on 1 HP and 2 HP motors under conditions of load torque variation and unbalanced voltages.

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References

  • Albla AA, Brkovic BM, Jecmenica MM, Lazarevic ZM (2017) Online temperature monitoring of a grid connected induction motor. Int J Electr Power Energy Syst 93((Supplement C)):276–282

    Article  Google Scholar 

  • AlThobiani F, Ball A, Choi BK (2013) An application to transient current signal based induction motor fault diagnosis of Fourier–Bessel expansion and simplified fuzzy artmap. Expert Syst Appl 40(13):5372–5384

    Article  Google Scholar 

  • Ameid T, Menacer A, Talhaoui H, Harzelli I (2017) Rotor resistance estimation using extended Kalman filter and spectral analysis for rotor bar fault diagnosis of sensorless vector control induction motor. Measurement 111((Supplement C)):243–59

    Article  Google Scholar 

  • Arqub OA (2017) Adaptation of reproducing kernel algorithm for solving fuzzy Fredholm–Volterra integrodifferential equations. Neural Comput Appl 28(7):1591–1610

    Article  Google Scholar 

  • Arqub OA, Abo-Hammour Z (2014) Numerical solution of systems of second-order boundary value problems using continuous genetic algorithm. Inf Sci 279:396–415

    Article  MathSciNet  Google Scholar 

  • Asfani D, Muhammad A, Syafaruddin Purnomo M, Hiyama T (2012) Temporary short circuit detection in induction motor winding using combination of wavelet transform and neural network. Expert Syst Appl 39(5):5367–5375

    Article  Google Scholar 

  • Bacha K, Salem SB, Chaari A (2012) An improved combination of Hilbert and Park transforms for fault detection and identification in three-phase induction motors. Int J Electr Power Energy Syst 43(1):1006–1016

    Article  Google Scholar 

  • Barzegaran M, Mazloomzadeh A, Mohammed OA (2013) Fault diagnosis of the asynchronous machines through magnetic signature analysis using finite-element method and neural networks. IEEE Trans Energy Convers 28(4):1064–1071

    Article  Google Scholar 

  • Bayram D, Şeker S (2015) Anfis model for vibration signals based on aging process in electric motors. Soft Comput 19(4):1107–1114

    Article  Google Scholar 

  • Bazan G, Scalassara P, Endo W, Goedtel A, Godoy W, Palácios R (2017) Stator fault analysis of three-phase induction motors using information measures and artificial neural networks. Electr Power Syst Res 143:347–356

    Article  Google Scholar 

  • Bellini A, Filippetti F, Tassoni C, Capolino GA (2008) Advances in diagnostic techniques for induction machines. IEEE Trans Ind Electron 55(12):4109–4126

    Article  Google Scholar 

  • Bilski P (2014) Application of support vector machines to the induction motor parameters identification. Measurement 51((Supplement C)):377–386

    Article  Google Scholar 

  • D’Angelo MF, Palhares RM, Cosme LB, Aguiar LA, Fonseca FS, Caminhas WM (2014) Incipient fault detection in induction machine stator-winding using a fuzzy-Bayesian change point detection approach. Appl Soft Comput 21((Supplement C)):647–653

    Article  Google Scholar 

  • Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evolut Comput 15(1):4–31

    Article  Google Scholar 

  • Das S, Koley C, Purkait P, Chakravorti S (2010) Wavelet aided svm classifier for stator inter-turn fault monitoring in induction motors. In: IEEE PES general meeting, 2010. IEEE, pp 1–6

  • Delgado-Arredondo P, Morinigo-Sotelo D, Osornio-Rios R, Avina-Cervantes J, Rostro-Gonzalez H, Romero-Troncoso R (2017) Methodology for fault detection in induction motors via sound and vibration signals. Mech Syst Signal Process 83:568–589

    Article  Google Scholar 

  • Drif M, Cardoso A (2014) Stator fault diagnostics in squirrel cage three-phase induction motor drives using the instantaneous active and reactive power signature analyses. IEEE Trans Ind Inform 10(2):1348–1360

    Article  Google Scholar 

  • Duan F, Živanović R (2013) Induction motor stator faults diagnosis by using parameter estimation algorithms. In: 2013 9th IEEE international symposium on diagnostics for electric machines, power electronics and drives (SDEMPED). IEEE, pp 274–280

  • Garcia-Ramirez AG, Morales-Hernandez LA, Osornio-Rios RA, Benitez-Rangel JP, Garcia-Perez A, de Jesus Romero-Troncoso R (2014) Fault detection in induction motors and the impact on the kinematic chain through thermographic analysis. Electr Power Syst Res 114:1–9

    Article  Google Scholar 

  • Ghate V, Dudul S (2010) Optimal MLP neural network classifier for fault detection of three phase induction motor. Expert Syst Appl 37(4):3468–3481

    Article  Google Scholar 

  • Guezmil A, Berriri H, Pusca R, Sakly A, Romary R, Mimouni MF (2017) Detecting inter-turn short-circuit fault in induction machine using high-order sliding mode observer: simulation and experimental verification. J Control Autom Electr Syst 28(4):532–540

    Article  Google Scholar 

  • Huang S, Yu H (2013) Intelligent fault monitoring and diagnosis in electrical machines. Measurement 46(9):3640–646

    Article  Google Scholar 

  • Kane P, Andhare A (2016) Application of psychoacoustics for gear fault diagnosis using artificial neural network. J Low Freq Noise Vib Active Control 35(3):207–220

    Article  Google Scholar 

  • Konar P, Chattopadhyay P (2015) Multi-class fault diagnosis of induction motor using Hilbert and wavelet transform. Appl Soft Comput 30:341–352

    Article  Google Scholar 

  • Krause PC, Wasynczuk O, Sudhoff SD, Pekarek S (2013) Analysis of electric machinery and drive systems, vol 75. Wiley, New York

    Book  Google Scholar 

  • Lima F, Kaiser W, da Silva IN, de Oliveira AA (2014) Open-loop neuro-fuzzy speed estimator applied to vector and scalar induction motor drives. Appl Soft Comput 21((Supplement C)):469–480

    Article  Google Scholar 

  • Price K, Storn RM, Lampinen JA (2006) Differential evolution: a practical approach to global optimization. Springer, Berlin

    MATH  Google Scholar 

  • Qin AK, Huang VL, Suganthan PN (2009) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evolut Comput 13(2):398–417

    Article  Google Scholar 

  • Riera-Guasp M, Antonino-Daviu JA, Capolino GA (2015) Advances in electrical machine, power electronic, and drive condition monitoring and fault detection: state of the art. IEEE Trans Ind Electron 62(3):1746–1759

    Article  Google Scholar 

  • Rodríguez PVJ, Arkkio A (2008) Detection of stator winding fault in induction motor using fuzzy logic. Appl Soft Comput 8(2):1112–1120

    Article  Google Scholar 

  • Sauer IL, Tatizawa H, Salotti FA, Mercedes SS (2015) A comparative assessment of Brazilian electric motors performance with minimum efficiency standards. Renew Sustain Energy Rev 41:308–318

    Article  Google Scholar 

  • Seshadrinath J, Singh B, Panigrahi BK (2014) Investigation of vibration signatures for multiple fault diagnosis in variable frequency drives using complex wavelets. IEEE Trans Power Electron 29(2):936–945

    Article  Google Scholar 

  • Singh A, Grant B, DeFour R, Sharma C, Bahadoorsingh S (2016) A review of induction motor fault modeling. Electr Power Syst Res 133:191–197

    Article  Google Scholar 

  • Treetrong J, Sinha JK, Gu F, Ball A (2012) Parameter estimation for electric motor condition monitoring. Adv Vib Eng 11(1):75–84

    Google Scholar 

  • Trigeassou JC (2013) Electrical machines diagnosis. Wiley, New York

    Google Scholar 

Download references

Acknowledgements

Author Alessandro Goedtel has received research Grants from National Council for Scientific and Technological Development—CNPq (Processes 474290/2008-5, 473576/2011-2, 552269/2011-5, 307220/2016-8) and Araucária Foundation of Support to the Scientific and Technological Development of Paraná (Process 06/56093-3).

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Correspondence to Jacqueline Jordan Guedes.

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Communicated by V. Loia.

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Guedes, J.J., Castoldi, M.F., Goedtel, A. et al. Differential evolution applied to line-connected induction motors stator fault identification. Soft Comput 23, 11217–11226 (2019). https://doi.org/10.1007/s00500-018-03674-w

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