To address challenges such as difficulty in fusing multiple data sources and insufficient consideration of sample correlations, this paper proposes a reinforced graph regularization fault diagnosis network that integrates data from multiple sensors for rolling bearing analysis. Initially, a deep convolutional neural network with multiple input channels is constructed to facilitate the fusion and feature extraction of sensor data. Subsequently, a reinforced graph regularization term is introduced to augment the network’s ability to learn geometric features. Finally, a two-stage training algorithm is designed to enhance operational efficiency and recognition accuracy, achieving fault identification in rolling bearings under the influence of multiple sensor data. The proposed approach is validated using a high-speed aviation-bearing data set from the Polytechnic University of Turin and an aero-engine bearing data set from Harbin Institute of Technology.
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