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A Combined Short Time Fourier Transform and Image Classification Transformer Model for Rolling Element Bearings Fault Diagnosis in Electric Motors

1
Electrical Machines Laboratory, Depterment of Electrical & Computer Engineering, Democritus University of Thrace, 671 00 Xanthi, Hellas, Greece
2
Department of Science and Technology, University Center of International Programmes of Studies, International Hellenic University, 570 01 Thermi, Hellas, Greece
*
Author to whom correspondence should be addressed.
Academic Editor: Andreas Holzinger
Mach. Learn. Knowl. Extr. 2021, 3(1), 228-242; https://0-doi-org.brum.beds.ac.uk/10.3390/make3010011
Received: 8 January 2021 / Revised: 10 February 2021 / Accepted: 11 February 2021 / Published: 16 February 2021
(This article belongs to the Section Learning)
The most frequent faults in rotating electrical machines occur in their rolling element bearings. Thus, an effective health diagnosis mechanism of rolling element bearings is necessary from operational and economical points of view. Recently, convolutional neural networks (CNNs) have been proposed for bearing fault detection and identification. However, two major drawbacks of these models are (a) their lack of ability to capture global information about the input vector and to derive knowledge about the statistical properties of the latter and (b) the high demand for computational resources. In this paper, short time Fourier transform (STFT) is proposed as a pre-processing step to acquire time-frequency representation vibration images from raw data in variable healthy or faulty conditions. To diagnose and classify the vibration images, the image classification transformer (ICT), inspired from the transformers used for natural language processing, has been suitably adapted to work as an image classifier trained in a supervised manner and is also proposed as an alternative method to CNNs. Simulation results on a famous and well-established rolling element bearing fault detection benchmark show the effectiveness of the proposed method, which achieved 98.3% accuracy (on the test dataset) while requiring substantially fewer computational resources to be trained compared to the CNN approach. View Full-Text
Keywords: bearing fault; convolutional neural network; electric motors; short time fourier transform; image classification transformer; fault diagnosis bearing fault; convolutional neural network; electric motors; short time fourier transform; image classification transformer; fault diagnosis
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MDPI and ACS Style

Alexakos, C.T.; Karnavas, Y.L.; Drakaki, M.; Tziafettas, I.A. A Combined Short Time Fourier Transform and Image Classification Transformer Model for Rolling Element Bearings Fault Diagnosis in Electric Motors. Mach. Learn. Knowl. Extr. 2021, 3, 228-242. https://0-doi-org.brum.beds.ac.uk/10.3390/make3010011

AMA Style

Alexakos CT, Karnavas YL, Drakaki M, Tziafettas IA. A Combined Short Time Fourier Transform and Image Classification Transformer Model for Rolling Element Bearings Fault Diagnosis in Electric Motors. Machine Learning and Knowledge Extraction. 2021; 3(1):228-242. https://0-doi-org.brum.beds.ac.uk/10.3390/make3010011

Chicago/Turabian Style

Alexakos, Christos T., Yannis L. Karnavas, Maria Drakaki, and Ioannis A. Tziafettas 2021. "A Combined Short Time Fourier Transform and Image Classification Transformer Model for Rolling Element Bearings Fault Diagnosis in Electric Motors" Machine Learning and Knowledge Extraction 3, no. 1: 228-242. https://0-doi-org.brum.beds.ac.uk/10.3390/make3010011

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