DEVELOPMENT AND COMPARISON OF FFT CURVE GENERATION MODELS FROM TEMPERATURE–TIME IMAGES FOR LUBRICATION CLASSIFICATION IN ROLLING BEARINGS
DOI:
https://doi.org/10.61164/j87vfa28Keywords:
Fast Fourier Transform, Rolling Bearings, Lubrication, Convolutional Neural Networks, Signal ProcessingAbstract
This study presents the development and comparison of five computational model variants designed to convert temperature–time curve images into frequency-domain representations using the Fast Fourier Transform (FFT). A total of 387 images were generated from .csv files of the KAIST Bearing Run-to-Failure Dataset (2020), representing three bearing lubrication regimes (healthy, marginal, and starved). Each model variant applies specific strategies for smoothing, energy-based weighting, and spectral enhancement to produce FFT curves suitable for subsequent classification by convolutional neural networks (CNNs). The results show that the FFT-Weighted, FFT-Composite, and FFT-1D-Plus versions achieved superior performance, generating spectra with both high global stability and local spectral detail. This balance enhances class differentiation by providing consistent textural and frequency-domain patterns for convolutional learning. The findings demonstrate that applying FFT to thermal signals increases data expressiveness and represents a promising preprocessing stage for CNN-based classification of bearing lubrication regimes.
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ACTUATORS. The Fault Diagnosis of Rolling Bearings Based on FFT SE TCN SVM. Actuators, v. 14, n. 3, p. 152, 2025. Disponível em: https://www.mdpi.com/2076-0825/14/3/152. Acesso em: 31 out. 2025.
CARVALHO, R. M.; CARVALHO, T. J. A.; DINIZ, B. C. Development of an Image Processing Model for Bearing Lubrication Classification Using Convolutional Neural Networks. In: XI SIINTEC – International Symposium on Innovative Technologies, 2025, Salvador. Proceedings: XI SIINTEC, 2025.
COOLEY, J. W.; TUKEY, J. W. An algorithm for the machine calculation of complex Fourier series. Mathematics of Computation, v. 19, n. 90, p. 297–301, 1965.
KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY (KAIST). Bearing Run-to-Failure Dataset. Daejeon, South Korea, 2020.
LECUN, Y.; BENGIO, Y.; HINTON, G. Deep learning. Nature, v. 521, n. 7553, p. 436–444, 2015.
OPPENHEIM, A. V.; SCHAFER, R. W. Discrete-Time Signal Processing. 3. ed. Upper Saddle River: Prentice Hall, 2010.
SENSORS. Bearing Fault Diagnosis Using Lightweight and Robust One Dimensional Convolution Neural Network in the Frequency Domain. Sensors, v. 22, n. 15, p. 5793, 2022. Disponível em: https://www.mdpi.com/1424-8220/22/15/5793. Acesso em: 31 out. 2025.
WU, X.; YANG, C.; ZHENG, H.; LI, X. Intelligent fault diagnosis of rotating machinery based on improved CNN with frequency–domain feature fusion. Sensors, v. 23, n. 8, p. 3921, 2023.
XU, M.; YU, Q.; CHEN, S.; LIN, J. Rolling Bearing Fault Diagnosis Based on CNN-LSTM with FFT and SVD. Information, v. 15, n. 7, p. 399, 2024. Disponível em: https://www.mdpi.com/2078-2489/15/7/399. Acesso em: 31 out. 2025.
ZHANG, H.; ZHOU, Y.; WANG, Z.; LI, J. Fault diagnosis of rolling bearings based on time–frequency representation and convolutional neural networks. Mechanical Systems and Signal Processing, v. 181, p. 109484, 2022.
ZHOU, J.; QIN, Y.; KOU, L.; YUWONO, M.; SU, S. Fault detection of rolling bearing based on FFT and classification. Journal of Advanced Mechanical Design, Systems, and Manufacturing, v. 9, n. 5, 2015. Disponível em: https://www.jstage.jst.go.jp/article/jamdsm/9/5/9_2015jamdsm0056/_article. Acesso em: 31 out. 2025.
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Copyright (c) 2025 Bruno da Cunha Diniz, Ranya Mota Carvalho, Talisson de Jesus Alves Carvalho
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