Dual data fusion fault diagnosis of transmission system based on entropy weighted multi-representation DS evidence theory and GCN

Power transmission reliability of drivelines guarantees fast maneuverability of heavy vehicles. During health monitoring, multi-sensor data fusion technology has been widely used in the improvement of fault diagnosis accuracy of long-link multi-structure drivelines. However, in multi-sensor fusion and joint fault diagnosis scenarios where multiple conditions coexist, it is still challenging to fuse multi-sensor data and extract generalized fault intrinsic features for any combination of operating conditions under multi-sensor monitoring. In this paper, a dual fusion graph convolutional network (DFGCN) is proposed for multi-sensor-multi-condition fault diagnosis of the transmission system. First, considering the data structure and the correlation of different sensors, DFGCN constructs multi-sensor intrinsic links synchronously from the data and feature levels by using multi-branch parallel GCN. Second, considering the susceptibility to over-confidence when the feature space of multi-condition data is inconsistent, an entropy-weighted multi-representation Dempster-Shafer (EWMR-DS) evidence theory fusion strategy is designed to extract the condition-shared features by increasing the label space diversity. Finally, an end-to-end lightweight diagnosis framework is scalable to multi-sensor and multi-working conditions in engineering practice, and the dual information fusion improves the fusion efficiency of fine-grained features with distributional differences. Using experimental datasets collected from two typical transmission fault test benches, the effectiveness of the proposed DFGCN method in multi-sensor-multi-condition scenarios is verified. The results indicate that DFGCN achieves an average diagnostic accuracy of more than 99.7% and superior noise resistance under different degrees of environmental noise.