An efficient bearing fault detection strategy based on a hybrid machine learning technique.

This study introduces an innovative method for addressing the bearing fault detection problem in rotating machinery. The proposed approach integrates multi-feature extraction, advanced feature selection, and state-of-the-art classification techniques using convolutional neural network (CNN) models. Leveraging the comprehensive Fault Bearing Dataset from Case Western Reserve University (CWRU), continuous wavelet transforms (CWT) and CNNs are utilized for feature extraction. The methodology also incorporates machine learning model tuning through Tree-Structured Parzen Estimators (TPE) for optimal hyperparameter adjustment, ensuring high-performance classification. Experimental results, based on the ResNet-50-SVM hybrid model, showed the effectiveness of the proposed approach in achieving an impressive accuracy of 95.51%. This confirms that the proposed methodology represents a significant advancement in bearing fault detection, providing an effective solution for predictive and preventive maintenance in industrial applications.