Bearing fault diagnosis method based on attention mechanism and multilayer fusion network

Abstract

The methods with multi-sensor data fusion have been a remarkable way to improve the accuracy and robustness of bearing fault diagnosis under complicated conditions. However, most of the existing fusion models or methods belong to single fusion level and simple fusion structure is usually utilized, and the correlation and complementarity of information between multi-sensor data might be easily ignored. In order to improve the performance of fault diagnosis with multi-sensor data fusion, this paper proposes a novel model of multi-layer deep fusion network with attention mechanism (AMMFN). The proposed model consists of a central network and multiple branch networks stacking by Inception networks, and the deep features of each single-sensor data are extracted automatically by the branch networks, and the extracted features of multi-sensor data at different levels are fused with the central network, and then the information interaction between multi-sensor data can be significantly enhanced and the adaptive hierarchical fusion of information can be achieved. Moreover, a fusion strategy based on attention mechanism is designed to extract more correlation information during the fusion of features extracted from multi-sensor data. Extensive experiments are also performed to evaluate the performance of proposed approach, and the comparison results with other methods indicate that the presented method takes higher accuracy and stronger generalization ability.