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Automatic progressive damage detection of rotor bar in induction motor using vibration analysis and multiple classifiers

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Abstract

There is an increased interest in developing reliable condition monitoring and fault diagnosis systems of machines like induction motors; such interest is not only in the final phase of the failure but also at early stages. In this paper, several levels of damage of rotor bars under different load conditions are identified by means of vibration signals. The importance of this work relies on a simple but effective automatic detection algorithm of the damage before a break occurs. The feature extraction is based on discrete wavelet analysis and autocorrelation process. Then, the automatic classification of the fault degree is carried out by a binary classification tree. In each node, comparing the learned levels of the breaking off correctly identifies the fault degree. The best results of classification are obtained employing computational intelligence techniques like support vector machines, multilayer perceptron, and the k-NN algorithm, with a proper selection of their optimal parameters.

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Authors and Affiliations

  1. Electronics Department, Instituto Nacional de Astrofísica, Óptica y Electrónica, Santa María Tonantzintla, Puebla, México

    Israel Cruz-Vega, Jose Rangel-Magdaleno & Juan Ramirez-Cortes

  2. Computer Science Department, Instituto Nacional de Astrofísica, Óptica y Electrónica, Santa María Tonantzintla, Puebla, México

    Hayde Peregrina-Barreto

Authors
  1. Israel Cruz-Vega
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  2. Jose Rangel-Magdaleno
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  3. Juan Ramirez-Cortes
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  4. Hayde Peregrina-Barreto
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Corresponding author

Correspondence to Jose Rangel-Magdaleno.

Additional information

Recommended by Associate Editor Byeng Dong Youn

Israel Cruz Vega received the B.Sc. in Control and Automation Engineering from the Instituto Politécnico Nacional, and M.Sc., as well as the Ph.D. in Automatic Control, from the Centro de Investigación y de Estudios Avanzados of the Instituto Politécnico Nacional in México, DF., in 2001, 2004 and 2011, respectively. He is currently a CONACYT Research Fellow commissioned to the Instituto Nacional de Astrofísica, Óptica y Électronica with the Electronics Department. His specific research interests are in the area of Automatic Control, Machine Learning, Intelligent Systems and Evolutionary Algorithms.

Jose de Jesus Rangel-Magdaleno received the B.E. in Electronics Engineering and the M.E. in Electrical Engineering in Hardware Signal Pocessing from Universidad de Guanajuato, Mexico in 2006 and 2008, respectively. He received the Ph.D. in Mechatronics from the Universidad Autonoma de Queretaro, Mexico in 2011. He is currently Titular Researcher at the Electronics Department at INAOE, Mexico. His research interests include FPGAs, signal and image processing, instrumentation and mechatronics.

Juan Manuel Ramirez-Cortes received the B.Sc. from the National Polytechnic Institute, Mexico, the M.Sc. from the National Institute of Astrophysics, Optics and Electronics (INAOE), Mexico, and the Ph.D. from Texas Tech University, all in electrical engineering. He is currently a Titular Researcher at the Electronics Department, INAOE, in Mexico, and member of the Mexican National Research System (SNI) level 1. His research interests include signal and image processing, neural networks, fuzzy logic, digital systems, and sensors.

Hayde Peregrina-Barreto received the B.Sc. from the Technological Institute of Cuautla, Mexico, the M.Sc. in Engineering from University of Guanajuato, Mexico, and the Ph.D. in Engineering from Autonomous University of Queretaro, Mexico. She is currently Titular Researcher in Computer Science at INAOE, Mexico, and member of the Mexican National Research System (SNI), level 1. Her research interests include signal and image processing, in the field of image processing include mathematical morphology, color appearance models, segmentations, and human visual perception.

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Cruz-Vega, I., Rangel-Magdaleno, J., Ramirez-Cortes, J. et al. Automatic progressive damage detection of rotor bar in induction motor using vibration analysis and multiple classifiers. J Mech Sci Technol 31, 2651–2662 (2017). https://doi.org/10.1007/s12206-017-0508-3

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  • DOI: https://doi.org/10.1007/s12206-017-0508-3

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