Fault diagnosis of bearings based on 1D-CNN and XGBoost algorithm integration

This study introduces a novel method for bearing fault diagnosis, merging one-dimensional convolutional neural networks (1D-CNN) with the XGBoost algorithm. We conducted detailed experiments using the Case Western Reserve University's bearing dataset. Our approach combines 1D-CNN's local pattern analysis in time series data with XGBoost's classification prowess, resulting in substantial performance improvements over existing diagnostic techniques. Notably, our method excels in recall rate, emphasizing its significance in enhancing the efficiency and reliability of bearing fault diagnosis.