Research on bearing fault diagnosis algorithm based on attention mechanism and dynamic convolutional neural network

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The proposed algorithm achieves 100% accuracy in fault diagnosis on noise-free datasets, improving traditional methods.
In noisy conditions, the accuracy remains high at 92%, showcasing the algorithm's robustness under varying conditions.
The method employs a residual network and dynamic convolution for deep feature extraction and temporal modeling.
These advances may reduce downtime and enhance operational efficiency in industrial production lines, indicating broad applicability.

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This algorithm improves diagnostic accuracy in bearings under variable conditions, suggesting enhanced maintenance strategies.

Peng et al. (2025) studied this question.

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