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Scientific Innovation in Asia

Volume 3, 2025 - Issue 3

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Vol. 3 (2025)
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Vol. 1 (2023)
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Open Access Journal Article

Intelligent Fault Diagnosis Framework for Bearings Based on a Hybrid CNN-LSTM-GRU Network

by Wei Ai 1
1
Maritime College, Tianjin University of Technology, Tianjin, China.
*
Author to whom correspondence should be addressed.
SIA  2025 3(3):54; https://doi.org/10.12410/sia0303002
Received: 2 June 2025 / Accepted: 17 June 2025 / Published Online: 28 June 2025
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Abstract

As one of the most important parts of rotating machinery, the early prediction of rolling bearing is very important for the safety of operation and cost reduction of maintenance. To improve the intelligent level of bearing fault recognition, this paper proposes an intelligent bearing fault diagnosis frame work based on a hybrid CNN-LSTM-GRU network, which combines the advantages of CNN, LSTM, and GRU, and makes structural innovation and optimization design on the basis of the existing deep learning model. The proposed model follows a three-segment hybrid structure, in the first segment, 1DCNN is employed to extract local time-domain feature from original vibration signal. Also, We feed the features that have been extracted by the CNN network as the inputs to the both LSTM network and GRU network in parallel in order to take care of the multiple dependencies between the sequence of data items and make the model optimize more efficiently, respectively. 5.fusion multi-head attention module is introduced to perform feature weighing for the dual-channel outputs, thus further enhancing the capability of identifying complicated failure modes. To prevent overfitting and coalescence while enhancing the generalization capability of the model, the final classification module is built upon Dropout regularized fully connected design with residual connection. Experiments on the CWRU bearing fault dataset demonstrate superior classification performance of the proposed network under different load and fault severities, providing an average accuracy of more than 94.2%.

Keywords: Fault diagnosis; Hybrid deep learning; CNN-LSTM-GRU; Time–frequency features; Attention mechanism.;
Copyright: © 2025 by Ai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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ACS Style
Ai, W. Intelligent Fault Diagnosis Framework for Bearings Based on a Hybrid CNN-LSTM-GRU Network. Scientific Innovation in Asia, 2025, 3, 54. doi:10.12410/sia0303002
AMA Style
Ai W. Intelligent Fault Diagnosis Framework for Bearings Based on a Hybrid CNN-LSTM-GRU Network. Scientific Innovation in Asia; 2025, 3(3):54. doi:10.12410/sia0303002
Chicago/Turabian Style
Ai, Wei 2025. "Intelligent Fault Diagnosis Framework for Bearings Based on a Hybrid CNN-LSTM-GRU Network" Scientific Innovation in Asia 3, no.3:54. doi:10.12410/sia0303002

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