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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\u2019s 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. 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