Intelligent Bearing Fault Diagnosis via Feature Fusion of Multi-Source Heterogeneous Data
Slewing bearings in low-speed, heavy-load equipment generate weak and heterogeneous fault signatures that are difficult to characterize using a single sensor. This study develops a compact dual-branch feature-fusion framework that jointly exploits six-channel vibration and one-channel acoustic-emission (AE) signals. To prevent source-record leakage, complete raw recording groups are assigned to training, validation, and test subsets before segmentation; non-overlapping 1024-point windows are then generated, and channel normalization is fitted using training groups only. Five matched random-seed runs are performed, with the best checkpoint selected exclusively by validation Macro-F1. The fusion model achieves mean test accuracy of 99.10% and Macro-F1 of 0.9910, compared with 97.38%/0.9738 for vibration-only and 98.03%/0.9802 for AE-only. The improvement over vibration-only is statistically significant (p = 0.022 for both Accuracy and Macro-F1), whereas the improvement over AE-only is numerical but does not reach the 0.05 significance level (p = 0.063). The fusion model also obtains the lowest mean Davies-Bouldin index (0.963). These results support compact vibration-AE fusion as an effective diagnostic baseline while also defining its statistical and deployment limitations.