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Overload Monitoring System Using Sound Analysis for Electrical Machines

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Artificial Intelligence and Soft Computing (ICAISC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13588))

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Abstract

Overloading is one of the faults that occur very often in the operation of electrical machines. Therefore, a continuous monitoring and diagnosis for this is necessary in safety-critical applications. This paper presents a sound analysis system used for detecting and classifying induction motor and power transformer overload levels with a microphone. Three acoustic features and six classification models are evaluated. The obtained results show that this is a promising way to monitor electrical machines overload.

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Author information

Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam

    Nguyen Cong-Phuong & Nguyen Tuan Ninh

Authors
  1. Nguyen Cong-Phuong
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  2. Nguyen Tuan Ninh
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Corresponding author

Correspondence to Nguyen Cong-Phuong .

Editor information

Editors and Affiliations

  1. Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Cong-Phuong, N., Ninh, N.T. (2023). Overload Monitoring System Using Sound Analysis for Electrical Machines. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2022. Lecture Notes in Computer Science(), vol 13588. Springer, Cham. https://doi.org/10.1007/978-3-031-23492-7_24

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  • DOI: https://doi.org/10.1007/978-3-031-23492-7_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23491-0

  • Online ISBN: 978-3-031-23492-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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