Abstract
The most popular rotating machine in the industry is the induction motor, and the harmful states on such motors may have consequences in costs, product quality, and safety. In this paper, a methodology that allows to detect faults in induction motors is proposed. Such methodology is based on the use of texture-inspired features in a fuzzy inference system. The features are extracted from the start-up current signal using the histograms of sum and differences, which have not been used for this kind of applications. The detected states in a given motor are: misalignment, motor with one broken bar and motor in good condition. The proposed methodology shows satisfactory results, using real signals of faulty motors, providing a new approach to detect faults in an automatic manner using only the current signals from the start-up stage.
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Acknowledgments
Calderon-Uribe would like to acknowledge for the grant provided by the Mexican National Council of Science and Technology (CONACyT). This research was supported by the University of Guanajuato and the PRODEP through the NPTC project with number DSA/103.5/15/7007.
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Calderon-Uribe, U., Lizarraga-Morales, R.A., Rodriguez-Donate, C., Cabal-Yepez, E. (2017). Detection of Faults in Induction Motors Using Texture-Based Features and Fuzzy Inference. In: Sidorov, G., Herrera-Alcántara, O. (eds) Advances in Computational Intelligence. MICAI 2016. Lecture Notes in Computer Science(), vol 10061. Springer, Cham. https://doi.org/10.1007/978-3-319-62434-1_23
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DOI: https://doi.org/10.1007/978-3-319-62434-1_23
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