Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/31317
Title: Multi-Scale Residual Convolutional Neural Network with Hybrid Attention for Bearing Fault Detection
Authors: Zhu, Y
Chen, W
Yan, S
Zhang, J
Zhu, C
Wang, F
Chen, Q
Keywords: motor fault;fault diagnosis;convolutional neural network (CNN);multi-scale residual network;hybrid attention mechanism
Issue Date: 14-May-2025
Publisher: MDPI
Citation: Zhu, Y. et al. (2025) 'Multi-Scale Residual Convolutional Neural Network with Hybrid Attention for Bearing Fault Detection', Machines, 13 (5), 413, pp. 1 - 19. doi: 10.3390/machines13050413.
Abstract: This paper proposes an advanced deep convolutional neural network model for motor bearing fault detection that was designed to overcome the limitations of traditional models in feature extraction, accuracy, and generalization under complex operating conditions. The model combines multi-scale residuals, hybrid attention mechanisms, and dual global pooling to enhance the performance. Convolutional layers efficiently extract features, while hybrid attention mechanisms strengthen the feature representation. The multi-scale residual network structure captures features at various scales, and fault classification is performed using global average and max pooling. The model was trained with the Adam optimizer and sparse categorical cross-entropy loss by incorporating a learning rate decay mechanism to refine the training process. Experiments on the University of Paderborn bearing dataset across four conditions showed that the model had superior performance, where it achieved a diagnostic accuracy of 99.7%, which surpassed traditional models, like AMCNN, LeNet5, and AlexNet. Comparative experiments on rolling bearing vibration and motor current datasets across four bearing conditions highlighted the model’s effectiveness and broad applicability in motor fault detection. Its robust feature extraction and classification capabilities make it a reliable solution for motor bearing fault diagnosis, with significant potential for real-world applications. This makes it a reliable solution for motor bearing fault diagnosis with significant potential for practical applications.
Description: Data Availability Statement: The data presented in this paper are available upon request from the corresponding author. The data are not publicly available due to considerations of privacy protection and ethical principles.
URI: https://bura.brunel.ac.uk/handle/2438/31317
DOI: https://doi.org/10.3390/machines13050413
Other Identifiers: ORCiD: Yanping Zhu https://orcid.org/0000-0002-8107-0101
ORCiD: Fang Wang https://orcid.org/0000-0003-1987-9150
Article number: 413
Appears in Collections:Dept of Computer Science Research Papers

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