(untitled) (REMUNOM, 2025)
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.