1D Convolution and Residual Modules: How Rolling Bearing Fault Diagnosis Accuracy Improved from 83% to 95%

In rolling bearing fault diagnosis, a single-layer 1D-CNN achieves a test accuracy of only about 83%, and inner race fault samples are often misclassified as rolling element faults; after switching to a CNN+ResNet with 5 residual blocks, the accuracy rises to 95%+. The residual connection, with its shortcut of output = F(x) + x, provides a lossless channel for gradients, solving the vanishing gradient and degradation problems of deep networks and enabling the network to distinguish inner race faults from rolling element faults after deepening. The two sets of experiments use the same data, splits, and evaluation method; the only difference is whether residuals are added.