DEVELOPMENT AND COMPARISON OF FFT CURVE GENERATION MODELS FROM TEMPERATURE–TIME IMAGES FOR LUBRICATION CLASSIFICATION IN ROLLING BEARINGS (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.