Intelligent Bearing Fault Diagnosis via Feature Fusion of Multi-Source Heterogeneous Data

Abstract

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.

Conflict of Interest Statement
The authors declare no conflicts of interest.