Soft Computing

, Volume 21, Issue 22, pp 6673–6685 | Cite as

Bearing fault identification of three-phase induction motors bases on two current sensor strategy

  • Tiago Drummond Lopes
  • Alessandro Goedtel
  • Rodrigo Henrique Cunha Palácios
  • Wagner Fontes Godoy
  • Roberto Molina de Souza
Methodologies and Application
  • 176 Downloads

Abstract

Three-phase induction motors are the most commonly used devices for electromechanical energy conversion. This study proposes an alternative approach for identifying bearing faults in induction motors, using two current sensors and a pattern classifier, based on artificial neural networks. To validate the methodology, results are given from experiments carried out on a test bench where the motors operate with different types of bearing faults, under varying conditions of load torque and voltage unbalance. This paper also provides the comparative performance of neural network and random forest classifiers. This study also presents an analysis of the current signals in the time domain, applied to different neural structures.

Keywords

Three-phase induction motor Bearing faults Artificial neural network 

References

  1. Barzegaran M, Mazloomzadeh A, Mohammed O (2013) Fault diagnosis of the asynchronous machines through magnetic signature analysis using finite-element method and neural networks. IEEE Trans Energy Convers 28(4):1064–1071CrossRefGoogle Scholar
  2. Bayram D, Şeker S (2013) Wavelet based neuro-detector for low frequencies of vibration signals in electric motors. Appl Soft Comput 13(5):2683–2691CrossRefGoogle Scholar
  3. Bayram D, Şeker S (2015) Anfis model for vibration signals based on aging process in electric motors. Soft Comput 19(4):1107–1114CrossRefGoogle Scholar
  4. Bellini A, Filippetti F, Tassoni C, Capolino GA (2008) Advances in diagnostic techniques for induction machines. IEEE Trans Ind Electron 55(12):4109–4126CrossRefGoogle Scholar
  5. Bollen M (2000) Understanding power quality problems: voltage sags and interruptions. IEEE—TP 139-0. WileyGoogle Scholar
  6. Breiman L (2001) Random forests. Mach Learn 45(1):5–32CrossRefMATHGoogle Scholar
  7. Cococcioni M, Lazzerini B, Volpi S (2013) Robust diagnosis of rolling element bearings based on classification techniques. IEEE Trans Ind Inf 9(4):2256–2263CrossRefGoogle Scholar
  8. D’Angelo MF, Palhares RM, Cosme LB, Aguiar LA, Fonseca FS, Caminhas WM (2014) Fault detection in dynamic systems by a fuzzy/bayesian network formulation. Appl Soft Comput 21:647–653CrossRefGoogle Scholar
  9. do Nascimento CF, de Oliveira AA Jr, Goedtel A (2011) Harmonic identification using parallel neural networks in single-phase systems. Appl Soft Comput 11(2):2178–2185CrossRefGoogle Scholar
  10. Frosini L, Bassi E (2010) Stator current and motor efficiency as indicators for different types of bearing faults in induction motors. IEEE Trans Ind Electron 57(1):244–251CrossRefGoogle Scholar
  11. Frosini L, Harliśca C, Szabó L (2015) Induction machine bearing fault detection by means of statistical processing of the stray flux measurement. IEEE Trans Ind Electron 62(3):1846–1854CrossRefGoogle Scholar
  12. Garcia-Perez A, Romero-Troncoso RJ, Cabal-Yepez E, Osornio-Rios RA, Lucio-Martinez JA (2012) Application of high-resolution spectral analysis for identifying faults in induction motors by means of sound. J Vib Control 18(11):1585–1594CrossRefGoogle Scholar
  13. Garcia-Ramirez A, Morales-Hernandez L, Osornio-Rios R, Garcia-Perez A, Romero-Troncoso R et al (2014a) Thermographic technique as a complement for mcsa in induction motor fault detection. In: International conference on electrical machines (ICEM), pp 1940–1945Google Scholar
  14. Garcia-Ramirez AG, Morales-Hernandez LA, Osornio-Rios RA, Benitez-Rangel JP, Garcia-Perez A, de Jesus Romero-Troncoso R (2014b) Fault detection in induction motors and the impact on the kinematic chain through thermographic analysis. Electr Power Syst Res 114:1–9CrossRefGoogle Scholar
  15. Ghate VN, Dudul SV (2010) Optimal mlp neural network classifier for fault detection of three phase induction motor. Exp Syst Appl 37(1):3468–3481CrossRefGoogle Scholar
  16. Godoy W, da Silva I, Goedtel A, Palácios R, Gongora W et al (2014) Neural approach for bearing fault classification in induction motors by using motor current and voltage. In: 2014 International joint conference on neural networks (IJCNN), pp 2087–2092Google Scholar
  17. Godoy WF, da Silva IN, Goedtel A, Palácios RHC (2015) Evaluation of stator winding faults severity in inverter-fed induction motors. Appl Soft Comput 32:420–431CrossRefGoogle Scholar
  18. Haykin S (1998) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall, Upper Saddle RiverMATHGoogle Scholar
  19. Hollander M, Wolfe DA (1999) Nonparametric statistical methods. Wiley series in probability and statistics. Wiley, New York. http://opac.inria.fr/record=b1095753, a Wiley-Interscience publication
  20. Leite V, Borges da Silva J, Cintra Veloso G, Borges da Silva L, Lambert-Torres G, Bonaldi E, Lacerda De, de Oliveira L (2015) Detection of localized bearing faults in induction machines by spectral kurtosis and envelope analysis of stator current. IEEE Trans Ind Electron 62(3):1855–1865CrossRefGoogle Scholar
  21. Palácios RHC, Silva IN, Goedtel A, Godoy WF, Oleskovicz M (2014) A robust neural method to estimate torque in three-phase induction motor. J Control Autom Electr Syst 25(4):493–502Google Scholar
  22. Palácios RHC, da Silva IN, Goedtel A, Godoy WF (2015) A comprehensive evaluation of intelligent classifiers for fault identification in three-phase induction motors. Electr Power Syst Res 127:249–258CrossRefGoogle Scholar
  23. Palácios RHC, da Silva IN, Goedtel A, Godoy WF (2016) A novel multi-agent approach to identify faults in line connected three-phase induction motors. Appl Soft Comput 45:1–10CrossRefGoogle Scholar
  24. Pandya D, Upadhyay S, Harsha S (2014) Fault diagnosis of rolling element bearing by using multinomial logistic regression and wavelet packet transform. Soft Comput 18(2):255–266CrossRefGoogle Scholar
  25. Pichler K, Lughofer E, Pichler M, Buchegger T, Klement EP, Huschenbett M (2016) Fault detection in reciprocating compressor valves under varying load conditions. Mech Syst Signal Process 70, 71:104–119CrossRefGoogle Scholar
  26. Popescu M, Dorrell D, Alberti L, Bianchi N, Staton D, Hawkins D (2013) Thermal analysis of duplex three-phase induction motor under fault operating conditions. IEEE Trans Ind Appl 49(4):1523–1530CrossRefGoogle Scholar
  27. Prieto M, Cirrincione G, Espinosa A, Ortega J, Henao H (2013) Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks. IEEE Trans Ind Electron 60(8):3398–3407CrossRefGoogle Scholar
  28. Roshanfekr R, Jalilian A (2015) Analysis of rotor and stator winding inter-turn faults in wrim using simulated mec model and experimental results. Electr Power Syst Res 119:418–424CrossRefGoogle Scholar
  29. Saidi L, Ali JB, Fnaiech F (2015) Application of higher order spectral features and support vector machines for bearing faults classification. ISA Trans 54:193–206CrossRefGoogle Scholar
  30. Shapiro SS, Wilk MB (1965) An analysis of variance test for normality (complete samples). Biometrika 52(3/4):591–611MathSciNetCrossRefMATHGoogle Scholar
  31. Son JD, Niu G, Yang BS, Hwang DH, Kang DS (2009) Development of smart sensors system for machine fault diagnosis. Expert Syst Appl 36(9):11981–11991CrossRefGoogle Scholar
  32. Sprooten J, Maun JC (2009) Influence of saturation level on the effect of broken bars in induction motors using fundamental electromagnetic laws and finite element simulations. IEEE Trans Energy Convers 24(3):557–564CrossRefGoogle Scholar
  33. Tran VT, 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):5372–5384CrossRefGoogle Scholar
  34. Vakharia V, Gupta V, Kankar P (2015) A comparison of feature ranking techniques for fault diagnosis of ball bearing. Soft Comput 20(4):1601–1619Google Scholar
  35. Zarei J, Tajeddini MA, Karimi HR (2014) Vibration analysis for bearing fault detection and classification using an intelligent filter. Mechatronics 24(2):151–157CrossRefGoogle Scholar
  36. Zavoianu AC, Bramerdorfer G, Lughofer E, Silber S, Amrhein W, Klement EP (2013) Hybridization of multi-objective evolutionary algorithms and artificial neural networks for optimizing the performance of electrical drives. Eng Appl Artif Intell 26(8):1781–1794CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Departments of Electrical and Computer Engineering and MathematicsFederal Technological University of Paraná (UTFPR)Cornélio ProcópioBrazil

Personalised recommendations