Indirect monitoring and early detection of faults in trains' motors

Indirect monitoring and early detection of faults in trains' motors

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This study investigates the ability of temperature sensors installed in the traction core of trains to early detect incipient faults. For instance, the breaking of a bearing is known to be critical as it may cause an increase of the temperature in the motor compartment, that in turn may eventually lead to a winding fault in the induction motor. The technique proposed in this contribution is characterised by extreme generality, since most frequent incipient faults lead to temperature increase that, if properly analysed, can be a tool for preventive maintenance. In particular, the measured data, provided by the main Italian railway company, are processed by two different methodologies which are characterised by positive, yet different, performances. The results show that preventive maintenance with the proposed approach is feasible.


    1. 1)
      • 1. Hodge, V.J., O'Keefe, S., Weeks, M., et al: ‘Wireless sensor networks for condition monitoring in the railway industry: a survey’, IEEE Trans. Intell. Transp. Syst., 2015, 16, (3), pp. 10881106.
    2. 2)
      • 2. Henao, H., Capolino, G.-A., Fernandez Cabanas, M., et al: ‘Trends in fault diagnosis for electrical machines: a review of diagnostic techniques’, IEEE Ind. Electron. Mag., 2014, 8, (2), pp. 3142.
    3. 3)
      • 3. Garcia-Escudero, L.A., Duque-Perez, O., Morinigo-Sotelo, D., et al: ‘Robust condition monitoring for early detection of broken rotor bars in induction motors’, Expert Syst. Appl., 2011, 38, (3), pp. 26532660.
    4. 4)
      • 4. Garcia-Escudero, L.A., Duque-Perez, O., Fernandez-Temprano, M., et al: ‘Robust detection of incipient faults in VSI-fed induction motors using quality control charts’, IEEE Trans. Ind. Appl., 2016, PP, (99), pp. 11.
    5. 5)
      • 5. Ghate, V.N., Dudul, S.V.: ‘Cascade neural-network-based fault classifier for three-phase induction motor’, IEEE Trans. Ind. Electron., 2011, 58, (5), pp. 15551563.
    6. 6)
      • 6. Su, H., Chong, K.T.: ‘Induction machine condition monitoring using neural network modeling’, IEEE Trans. Ind. Electron., 2007, 54, (1), pp. 241249.
    7. 7)
      • 7. Prieto, M.D., Cirrincione, G., Espinosa, A.G., et al: ‘Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks’, IEEE Trans. Ind. Electron., 2013, 60, (8), pp. 33983407.
    8. 8)
      • 8. Garcia-Ramirez, A.G., Morales-Hernandez, L.A., Osornio-Rios, R.A., et al: ‘Fault detection in induction motors and the impact on the kinematic chain through thermographic analysis’, Electr. Power Syst. Res., 2014, 114, pp. 19.
    9. 9)
      • 9. Picazo-Rodenas, M.J., Royo, R., Antonino-Daviu, J., et al: ‘Use of the infrared data for heating curve computation in induction motors: application to fault diagnosis’, Eng. Failure Anal., 2013, 35, (15), pp. 178192.
    10. 10)
      • 10. Singh, G.K, Al Kazzaz, S.A.S.: ‘Induction machine drive condition monitoring and diagnostic research – a survey’, Electric Power Syst. Res., 2003, 64, (2), pp. 146168.
    11. 11)
      • 11. Bellini, A., Filippetti, F., Tassoni, C., et al: ‘Advances in diagnostic techniques for induction machines’, IEEE Trans. Ind. Electron., 2008, 55, (12), pp. 41094126.
    12. 12)
      • 12. Grubic, S., Aller, J.M., Lu, B., et al: ‘A survey on testing and monitoring methods for stator insulation systems of low-voltage induction machines focusing on turn insulation problems’, IEEE Trans. Ind. Electron., 2008, 55, (12), pp. 41274136.
    13. 13)
      • 13. Zhang, P., Du, Y., Habetler, T.G., et al: ‘A survey of condition monitoring and protection methods for medium-voltage induction motors’, IEEE Trans. Ind. Appl., 2011, 47, (1), pp. 3446.
    14. 14)
      • 14. Zhou, W., Habetler, T.G., Harley, R.G.: ‘Bearing condition monitoring methods for electric machines: a general review’. Proc. IEEE Int. Symp. Diagnostics for Electric Machines, Power Electronics and Drives, Cracow, Poland, 2007, pp. 36.
    15. 15)
      • 15. Maru, B., Zotos, P.A.: ‘Anti-friction bearing temperature rise for NEMA frame motors’, IEEE Trans. Ind. Appl., 2011, 25, (5), pp. 883888.
    16. 16)
      • 16. Nejikovsky, B., Keller, E.: ‘Wireless communications based system to monitor performance of rail vehicles’. Proc. 2000 ASME/IEEE Joint Railroad Conf., Newark, NJ, 2000, pp. 111124.
    17. 17)
      • 17. Dietterich, T.G.: ‘Ensemble methods in machine learning’. Proc. First Int. Workshop on Multiple Classifier Systems, Cagliari, Italy, 2000, pp. 115.
    18. 18)
      • 18. Hotelling, H.: ‘Multivariate quality control – illustrated by the air testing of sample bombsights’, in Eisenhart, C., Hastay, M. W., Wallis, W. A. (Eds.): ‘Techniques of statistical analysis’ (McGraw-Hill, New York, 1947), pp. 111184.
    19. 19)
      • 19. Haykin, S.: ‘Neural networks: a comprehensive foundation’ (Prentice- Hall, Upper Saddle River, NJ, 1994).
    20. 20)
      • 20. Sun, Y., Kamel, M.S., Wong, A.K.C., et al: ‘Cost-sensitive boosting for classification of imbalanced data’, Pattern Recognit., 2007, 40, (2), pp. 33583378.
    21. 21)
      • 21. De Maesschalck, R., Jouan-Rimbaud, D., Massart, D.L.: ‘The Mahalanobis distance’, Chemometr. Intell. Lab. Syst., 2000, 50, (1), p. 118.
    22. 22)
      • 22. Aparisi, F., de Luna, M.A.: ‘The design of the multivariate synthetic-T2 control chart’, Commun. Stat. Theory Methods, 2009, 38, (2), pp. 173192.
    23. 23)
      • 23. Aparisi, F., Avendaño, G., Sanz, J.: ‘Techniques to interpret T2 control chart signals’, IIE Trans., 2006, 38, (8), pp. 647657.
    24. 24)
      • 24. Mason, R.L., Chou, Y.M., Young, J.C.: ‘Applying Hotelling's T2 statistic to batch processes’, J. Qual. Technol., 2001, 33, (4), pp. 466479.
    25. 25)
      • 25. Moya, M.M., Hush, D.R.: ‘Network constraints and multi-objective optimization for one-class classification’, Neural Netw., 1996, 9, (3), pp. 463474.

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