Deep Transfer Learning Approach Using Filtered Time-Frequency Representations of Current Signals for Bearing Fault Detection in Induction Machines
Loading...
Access rights
openAccess
CC BY
CC BY
publishedVersion
URL
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.
Date
Major/Subject
Mcode
Degree programme
Language
en
Pages
18
Series
IET Electric Power Applications, Volume 19, issue 1
Abstract
This paper addresses the challenge of limited labelled data in induction machine fault diagnosis by applying deep transfer learning with convolutional neural networks to classify ball bearing health conditions. Specifically, the objective is to classify ring and cage failures in ball bearings using a limited dataset acquired from an experimental test bench. Unlike traditional approaches that rely on vibration sensors, this study uses noninvasive current signals. Moreover, this study introduces a novel preprocessing approach that filters out the fundamental frequency of the current signal to enhance fault-related harmonics in time–frequency representations generated via continuous wavelet transform and short-time Fourier transform. Five pre-trained convolutional neural networks—ResNet18, ResNet50, VGG16, AlexNet and GoogLeNet—are fine-tuned on these representations, demonstrating up to a 47% improvement in classification accuracy. Furthermore, the approach maintains high accuracy even with only 10% of the original dataset, showcasing its sample efficiency. This work contributes to a scalable and data-efficient solution for reliable condition monitoring in industrial settings, further advancing the use of current signals for fault diagnosis.Description
Publisher Copyright: © 2025 The Author(s). IET Electric Power Applications published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
Other note
Citation
El Bouharrouti, N, Nemat Saberi, A, Khan, M, Kudelina, K, Naseer, M U & Belahcen, A 2025, 'Deep Transfer Learning Approach Using Filtered Time-Frequency Representations of Current Signals for Bearing Fault Detection in Induction Machines', IET Electric Power Applications, vol. 19, no. 1, e70074. https://doi.org/10.1049/elp2.70074