Abstract
The monitoring condition of electrical machine is an important parameter for maintenance of industrial process operation levels. In this paper, an investigation based on learning machine classifiers to proper classify machine multiple faults i.e. stator short-circuits, broken rotor bars and bearings in three phase induction motors driven by different inverters models is proposed. Experimental tests were performed in 2 different motors, running at steady state, operating under variable speed and torque variation resulting in 2967 samples. The main concept of proposed approach is to apply the three phase current amplitudes to immediately detect motor operating conditions. The high dimensionality of the input vectors in the algorithms was solved through the discretization of the current data, which allows the reduction the classification complexity providing a optimized waveform in comparison with the original one. The results show that it is possible to classify accurately these faults.
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References
Seera, M., Lim, C.P., Nahavandi, S., Loo, C.K.: Condition monitoring of induction motors: a review and an application of an ensemble of hybrid intelligent models. Expert. Syst. Appl. 41(10), 4891–4903 (2014)
Seshadrinath, J., Singh, B., Panigrahi, B.: Investigation of vibration signatures for multiple fault diagnosis in variable frequency drives using complex wavelets. IEEE Trans. Power Electron. 29(2), 936–945 (2014)
Zarei, J., Tajeddini, M.A., Karimi, H.R.: Vibration analysis for bearing fault detection and classification using an intelligent filter. Mechatronics 24(2), 151–157 (2014)
Das, S., Purkait, P., Koley, C., Chakravorti, S.: Performance of a load-immune classifier for robust identification of minor faults in induction motor stator winding. IEEE Trans. Dielectr. Electr. Insul. 21(1), 33–44 (2014)
Jia, F., Lei, Y., Lin, J., Zhou, X., Lu, N.: Deep neural networks: a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mech. Syst. Signal Process. 72–73, 303–315 (2016)
Xu, Z., Li, Y., Wang, Z., Xuan, J.: A selective fuzzy artmap ensemble and its application to the fault diagnosis of rolling element bearing. Neurocomputing 182, 25–35 (2016)
Baraldi, P., Cannarile, F., Maio, F.D., Zio, E.: Hierarchical k-nearest neighbours classification and binary differential evolution for fault diagnostics of automotive bearings operating under variable conditions. Eng. Appl. Artif. Intell. 56, 1–13 (2016)
Riera-Guasp, M., Antonino-Daviu, J.A., Capolino, G.A.: 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 (2015)
Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., Siegel, D.: Prognostics and health management design for rotary machinery systems–reviews, methodology and applications. Mech. Syst. Signal Process. 42(1–2), 314–334 (2014)
Bellini, A., Filippetti, F., Tassoni, C., Capolino, G.A.: Advances in diagnostic techniques for induction machines. IEEE Trans. Ind. Electron. 55(12), 4109–4126 (2008)
Maruthi, G.S., Hegde, V.: Application of MEMS accelerometer for detection and diagnosis of multiple faults in the roller element bearings of three phase induction motor. IEEE Sens. J. 16(1), 145–152 (2016)
do Nascimento, C.F., de Oliveira Jr., A.A., Goedtel, A., Serni, P.J.A.: Harmonic identification using parallel neural networks in single-phase systems. Appl. Soft Comput. 11(2), 2178–2185 (2011)
Lughofer, E., Buchtala, O.: Reliable all-pairs evolving fuzzy classifiers. IEEE Trans. Fuzzy Syst. 21(4), 625–641 (2013)
Lughofer, E., Weigl, E., Heidl, W., Eitzinger, C., Radauer, T.: Integrating new classes on the fly in evolving fuzzy classifier designs and their application in visual inspection. Appl. Soft Comput. 35, 558–582 (2015)
Haykin, S.O.: Neural Networks and Learning Machines, 3 edn. (2008). Hardcover
Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edn. Elsevier, Morgan Kaufmann, Amsterdam (2011)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Willey, New York (2001)
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The authors would like to thank the support and motivation provided by the Federal Technological University of Paraná.
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Godoy, W.F., Goedtel, A., da Silva, I.N., Palácios, R.H.C., L’Erario, A. (2019). Intelligent Systems Applied to the Classification of Multiple Faults in Inverter Fed Induction Motors. In: Thampi, S., Marques, O., Krishnan, S., Li, KC., Ciuonzo, D., Kolekar, M. (eds) Advances in Signal Processing and Intelligent Recognition Systems. SIRS 2018. Communications in Computer and Information Science, vol 968. Springer, Singapore. https://doi.org/10.1007/978-981-13-5758-9_10
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DOI: https://doi.org/10.1007/978-981-13-5758-9_10
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