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Unsupervised Deep Learning for Induction Motor Bearings Monitoring

  • Francesca Cipollini
  • Luca OnetoEmail author
  • Andrea Coraddu
  • Stefano Savio
APPLICATION
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Abstract

Induction motors are fundamental components of several modern automation system, and they are one of the central pivot of the developing e-mobility era. The most vulnerable parts of an induction motor are the bearings, the stator winding, and the rotor bars. Consequently, monitoring and maintaining them during operations is vital. In this work, authors propose an induction motor bearings monitoring tool which leverages on stator currents signals processed with a deep learning architecture. Differently from the state-of-the-art approaches which exploit vibration signals, collected by easily damageable and intrusive vibration probes, the stator currents signals are already commonly available, or easily and unintrusively collectable. Moreover, instead of using now-classical data-driven models, authors exploit a deep learning architecture able to extract from the stator current signal a compact and expressive representation of the bearings state, ultimately providing a bearing fault detection system. In order to estimate the effectiveness of the proposal, authors collected a series of data from an inverter-fed motor mounting different artificially damaged bearings. Results show that the proposed approach provides a promising and effective yet simple bearing fault detection system.

Keywords

Deep learning Monitoring Induction motors Bearings Stator currents 

References

  1. 1.
    D. Anguita, A. Ghio, L. Oneto, X. Parra, J.L. Reyes-Ortiz, in A public domain Ddtaset for human activity recognition using smartphones. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (2013)Google Scholar
  2. 2.
    D. Anguita, A. Ghio, L. Oneto, S. Ridella, In-sample and out-of-sample model selection and error estimation for support vector machines. IEEE Trans. Neural Netw. Learn. Syst. 23(9), 1390–1406 (2012)CrossRefGoogle Scholar
  3. 3.
    B. Ayhan, M.Y. Chow, M.H. Song, Multiple discriminant analysis and neural-network-based monolith and partition fault-detection schemes for broken rotor bar in induction motors. IEEE Trans. Ind. Electron. 53(4), 1298–1308 (2006)CrossRefGoogle Scholar
  4. 4.
    M.E.H. Benbouzid, G.B. Kliman, What stator current processing-based technique to use for induction motor rotor faults diagnosis? IEEE Trans. Energy Convers. 18(2), 238–244 (2003)CrossRefGoogle Scholar
  5. 5.
    Y. Bengio, A. Courville, P. Vincent, Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)CrossRefGoogle Scholar
  6. 6.
    J. Bergstra, Y. Bengio, Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13, 281–305 (2012)MathSciNetzbMATHGoogle Scholar
  7. 7.
    M. Blodt, P. Granjon, B. Raison, G. Rostaing, Models for bearing damage detection in induction motors using stator current monitoring. IEEE Trans. Ind. Electron. 55(4), 1813–1822 (2008)CrossRefGoogle Scholar
  8. 8.
    F. Chollet, et al., Keras. https://keras.io (2015)
  9. 9.
    A. Coraddu, L. Oneto, A. Ghio, S. Savio, D. Anguita, M. Figari, Machine learning approaches for improving condition-based maintenance of naval propulsion plants. Proc. Inst. Mech. Eng. B: J. Eng. Marit. Environ. 230(1), 136–153 (2016)CrossRefGoogle Scholar
  10. 10.
    R. Dekker, Applications of maintenance optimization models: a review and analysis. Reliab. Eng. Syst. Saf. 51(3), 229–240 (1996)CrossRefGoogle Scholar
  11. 11.
    B. Efron, R.J. Tibshirani. An Introduction to the Bootstrap (CRC Press, Boca Raton, 1994)zbMATHGoogle Scholar
  12. 12.
    F. Filippetti, G. Franceschini, C. Tassoni, P. Vas, Recent developments of induction motor drives fault diagnosis using ai techniques. IEEE Trans. Ind. Electron. 47(5), 994–1004 (2000)CrossRefGoogle Scholar
  13. 13.
    L. Frosini, E. Bassi, Stator current and motor efficiency as indicators for different types of bearing faults in induction motors. IEEE Trans. Ind. Electron. 57(1), 244–251 (2010)CrossRefGoogle Scholar
  14. 14.
    A. García-Gamboa, N. Hernández-Gress, M. González-Mendoza, R. Ibarra-Orozco, J. Mora-Vargas, in A comparison of different initialization strategies to reduce the training time of support vector machines. International Conference on Artificial Neural Networks (2005)Google Scholar
  15. 15.
    N. Gebraeel, M. Lawley, R. Liu, V. Parmeshwaran, Residual life predictions from vibration-based degradation signals: a neural network approach. IEEE Trans. Ind. Electron. 51(3), 694–700 (2004)CrossRefGoogle Scholar
  16. 16.
    I. Guyon, A. Elisseeff, An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)zbMATHGoogle Scholar
  17. 17.
    R.H.R. Hahnloser, R. Sarpeshkar, M.A. Mahowald, R.J. Douglas, H.S. Seung, Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit. Nature. 405(6789), 947–951 (2000)CrossRefGoogle Scholar
  18. 18.
    M. Hardt, J. Ullman, in Preventing false discovery in interactive data analysis is Hard. Annual Symposium on Foundations of Computer Science (2014)Google Scholar
  19. 19.
    G.E. Hinton, S. Osindero, Y.W. Teh, A fast learning algorithm for deep belief nets. Neural Comput. 18 (7), 1527–1554 (2006)MathSciNetCrossRefGoogle Scholar
  20. 20.
    F. Immovilli, M. Cocconcelli, A. Bellini, R. Rubini, Detection of generalized-roughness bearing fault by spectral-kurtosis energy of vibration or current signals. IEEE Trans. Ind. Electron. 56(11), 4710–4717 (2009)CrossRefGoogle Scholar
  21. 21.
    Mechanical vibration measurement and evaluation of machine vibration part 1: general guidelines. Standard, International Organization for Standardization, Geneva (2016)Google Scholar
  22. 22.
    S. Karmakar, S. Chattopadhyay, M. Mitra, S. Sengupta. Induction Motor Fault Diagnosis: Approach Through Current Signature Analysis (Springer, Berlin, 2016)CrossRefGoogle Scholar
  23. 23.
    G.B. Kliman, J. Stein, in Induction motor fault detection via passive current monitoring—a Brief Survey. Meeting of the Mechanical Failures Prevention Group (1990), pp. 49–65Google Scholar
  24. 24.
    G.B. Kliman, J. Stein, Methods of motor current signature analysis. Electr. Mach. Power Syst. 20(5), 463–474 (1992)CrossRefGoogle Scholar
  25. 25.
    C.T. Kowalski, T. Orlowska-Kowalska, Neural networks application for induction motor faults diagnosis. Math. Comput. Simul. 63(3), 435–448 (2003)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition. Proc. IEEE. 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  27. 27.
    S. Nandi, H.A. Toliyat, X. Li, Condition monitoring and fault diagnosis of electrical motors?a review. IEEE Trans. Energy Convers. 20(4), 719–729 (2005)CrossRefGoogle Scholar
  28. 28.
    J. Ngiam, A. Coates, A. Lahiri, B. Prochnow, Q.V. Le, A.Y. Ng, in On ptimization methods for deep learning. International Conference on Machine Learning (2011)Google Scholar
  29. 29.
    I.Y. Önel, K.B. Dalci, I. Senol, Detection of bearing defects in three-phase induction motors using Park’s transform and radial basis function neural networks. Sadhana 31(3) (2006)Google Scholar
  30. 30.
    K. Pearson, Principal components analysis. London, Edinb. Dublin Philos. Mag. J. 6(2), 566 (1901)Google Scholar
  31. 31.
    M.D. Prieto, G. Cirrincione, A.G. Espinosa, J.A. Ortega, H. Henao, Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks. IEEE Trans. Ind. Electron. 60(8), 3398–3407 (2013)CrossRefGoogle Scholar
  32. 32.
    F. Rosenblatt, The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65(6), 386 (1958)CrossRefGoogle Scholar
  33. 33.
    D.E. Rumelhart, G.E. Hinton, R.J. Williams, Learning representations by back-propagating errors. Cogn. Model. 5(3), 1 (1988)zbMATHGoogle Scholar
  34. 34.
    B. Samanta, K.R. Al-Balushi, S.A. Al-Araimi, Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Eng. Appl. Artif. Intell. 16(7), 657–665 (2003)CrossRefGoogle Scholar
  35. 35.
    H. Saruhan, S. Sandemir, A. Çiçek, I. Uygur, Vibration analysis of rolling element bearings defects. J. Appl. Res. Technol. 12(3), 384–395 (2014)CrossRefGoogle Scholar
  36. 36.
    R. Schiltz, Forcing frequency identification of rolling element bearings. Sound Vib. 24(5), 16–19 (1990)Google Scholar
  37. 37.
    J. Schmidhuber, Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)CrossRefGoogle Scholar
  38. 38.
    R. Schoen, T. Habetler, F. Kamran, R. Bartfield, Motor bearing damage detection using stator current monitoring. IEEE Trans. Ind. Appl. 31(6), 1274–1279 (1995)CrossRefGoogle Scholar
  39. 39.
    R.R. Schoen, B.K. Lin, T.G. Habetler, J.H. Schlag, S. Farag, An unsupervised, on-line system for induction motor fault detection using stator current monitoring. IEEE Trans. Ind. Appl. 31(6), 1280–1286 (1995)CrossRefGoogle Scholar
  40. 40.
    J. Shawe-Taylor, N. Cristianini. Kernel methods for pattern analysis (Cambridge University Press, Cambridge, 2004)CrossRefGoogle Scholar
  41. 41.
    N. Srivastava, G.E. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)MathSciNetzbMATHGoogle Scholar
  42. 42.
    J. Stack, T. Habetler, R. Harley, Fault classification and fault signature production for rolling element bearings in electric machines. IEEE Trans. Ind. Appl. 40(3), 735–739 (2004)CrossRefGoogle Scholar
  43. 43.
    H. Su, K.T. Chong, Induction machine condition monitoring using neural network modeling. IEEE Trans. Ind. Electron. 54(1), 241–249 (2007)CrossRefGoogle Scholar
  44. 44.
    V. Sugumaran, V. Muralidharan, K.I. Ramachandran, Feature selection using decision tree and classification through proximal support vector machine for fault diagnostics of roller bearing. Mech. Syst. Signal Process. 21(2), 930–942 (2007)CrossRefGoogle Scholar
  45. 45.
    P. Vas. Parameter estimation, condition monitoring, and diagnosis of electrical machines (Clarendon Press, Oxford, 1993)Google Scholar
  46. 46.
    Z. Wang, C.S. Chang, X. German, W.W. Tan, in Online fault detection of induction motors using independent component analysis and fuzzy neural network. International Conference on Advances in Power System Control, Operation and Management (2009)Google Scholar
  47. 47.
    H. White, A reality check for data snooping. Econometrica. 68(5), 1097–1126 (2000)MathSciNetCrossRefGoogle Scholar
  48. 48.
    B. Yazici, G.B. Kliman, W.J. Premerlani, R.A. Koegl, G.B. Robinson, A. Abdel-Malek, in An adaptive, on-line, statistical method for bearing fault detection using stator current. Conference Record of the 1997 IEEE, Industry Applications Conference, Thirty-Second IAS Annual Meeting Industry Applications (1997)Google Scholar
  49. 49.
    W. Zhou, B. Lu, T.G. Habetler, R.G. Harley, Incipient bearing fault detection via motor stator current noise cancellation using wiener filter. IEEE Trans. Ind. Appl. 45(4), 1309–1317 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.DIBRISUniversity of GenovaGenovaItaly
  2. 2.Department of Naval Architecture, Ocean, Marine EngineeringUniversity of StrathclydeGlasgowUK
  3. 3.DITENUniversity of GenovaGenovaItaly

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