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

Jakub Poręba and Jerzy Baranowski
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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

Energies, 2022, vol. 15, issue 15, 1-12

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.

Keywords: functional data analysis; motor diagnostics; acoustic signal; functional logistic regression (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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