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Functional Logistic Regression for Motor Fault Classification Using Acoustic Data in Frequency Domain

Author

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  • Jakub Poręba

    (Department of Automatic Control and Robotics, AGH University of Science & Technology, 30-059 Kraków, Poland)

  • Jerzy Baranowski

    (Department of Automatic Control and Robotics, AGH University of Science & Technology, 30-059 Kraków, Poland)

Abstract

Motor diagnostics is an important subject for consideration. Electric motors of different types are present in a multitude of object, from consumer goods through everyday use devices to specialized equipment. Diagnostic assessment of motors using acoustic signals is an interesting field, as microphones are present everywhere and are relatively easy sensors to process. In this paper, we analyze acoustic signals for the purpose of motor diagnostics using functional data analysis. We represent the spectrum (FFT) of the acoustic signals on a B-Spline basis and construct a classifier based on that representation. The results are promising, especially for binary classifiers, while multiclass (softmax regression) shows more sensitivity to dataset size. In particular, we show that while we are able to obtain almost perfect classification for binary cases, multiclass classifiers can struggle depending on the training/testing split. This is especially visible for determining the number of broken teeth, which is a non-issue for binary classifiers.

Suggested Citation

  • Jakub Poręba & Jerzy Baranowski, 2022. "Functional Logistic Regression for Motor Fault Classification Using Acoustic Data in Frequency Domain," Energies, MDPI, vol. 15(15), pages 1-12, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5535-:d:876165
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    References listed on IDEAS

    as
    1. Philippe Besse & J. Ramsay, 1986. "Principal components analysis of sampled functions," Psychometrika, Springer;The Psychometric Society, vol. 51(2), pages 285-311, June.
    2. J. Ramsay, 1982. "When the data are functions," Psychometrika, Springer;The Psychometric Society, vol. 47(4), pages 379-396, December.
    3. Jerzy Baranowski & Katarzyna Grobler-Dębska & Edyta Kucharska, 2021. "Recognizing VSC DC Cable Fault Types Using Bayesian Functional Data Depth," Energies, MDPI, vol. 14(18), pages 1-17, September.
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