A Machine Learning Approach for Bearing Fault Identification Using IFWHT, GLCM, and Feature Ranking

Time–frequency analysis is necessary for condition monitoring of rotating machinery; comparatively limited attention has been given to image- based texture feature analysis derived from time–frequency representations. A bearing fault diagnosis model is proposed that combines the Inverse Fast Walsh-Hadamard Transform (IFWHT), texture feature extraction, feature ranking, and machine learning classification. First, IFWHT transforms vibration signals into time-frequency images, thereby improving the effective representation of transient and fault- related data. Based on these images, two-dimensional texture attributes are derived from the Gray Level Co-occurrence Matrix (GLCM), which records the spatial relationships and structural patterns of various bearing health conditions. The ReliefF algorithm ranks and selects the most discriminative features, reducing feature redundancy and improving classification performance. Machine learning classifiers are then trained and evaluated using the ranked feature set. The findings show that the suggested ranked feature-based framework provides consistent and good-quality fault classification performance. Fine KNN and Cubic SVM are among the compared models, where the former will reach a maximum classification accuracy of 93.8%, and the latter will reach an accuracy of 91.7% at various combinations of faults. The methodology suggested offers a strong and adaptable approach in the context of fault diagnosis, which effectively promotes predictive maintenance by lessening unforeseen downtimes and increasing the dependability of operations in industries.