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Sensors and Materials, Volume 36, Number 4(2) (2024)
Copyright(C) MYU K.K.

pp. 1419-1430
S&M3608 Research Paper of Special Issue
https://doi.org/10.18494/SAM4847
Published: April 19 , 2024

A Small Deep Learning Model for Fault Detection of a Broken Rotor Bar of an Induction Motor [PDF]

Pat Taweewat, Warachart Suwan-ngam, Kanoknuch Songsuwankit, and Poom Konghuayrob

(Received January 8, 2024; Accepted April 2, 2024)

Keywords: broken rotor bar detection, MCSA, FFT feature extraction, deep learning, model reductions

In this paper, we present an investigation of a small deep learning model applied to the detection of a broken rotor bar of an induction motor. The motor current spectrum analysis is the base method for fault detection. This proposed method focuses on the analysis of the modification of the input vector and model configuration. This method was implemented and it showed that the feature length and size of the model are reduced compared with the existing method. The experimental results showed that only feature extraction using the spectral-based method and limit range of its coefficient are adequate to provide accuracy of small deep learning comparable to that of the parallel-layer deep learning model. Likewise, at the same accuracy level, based on the deep learning model, a shorter sampling duration than that required by the reference model is needed.

Corresponding author: Poom Konghuayrob

This work is licensed under a Creative Commons Attribution 4.0 International License.

Cite this article
Pat Taweewat, Warachart Suwan-ngam, Kanoknuch Songsuwankit, and Poom Konghuayrob, A Small Deep Learning Model for Fault Detection of a Broken Rotor Bar of an Induction Motor, Sens. Mater., Vol. 36, No. 4, 2024, p. 1419-1430.