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Abstract Accurate fault diagnosis of rolling bearings under strong noise conditions remains challenging, as noise can significantly degrade feature quality. Although data-driven methods such as convolutional neural networks and Transformer-based models have shown strong feature extraction capabilities, their performance often deteriorates in low signal-to-noise ratio environments due to insufficient modeling of global correlations and loss of subtle fault-related information.
Open AccessArticle by Xianchao Guo 1, Yuchang Mo 1,* and Ke Yan 2 1 Fujian Province University Key Laboratory of Computational Science, Huaqiao University, Quanzhou 362021, China 2 Department of the Built Environment, College of Design and Engineering, National University of Singapore, Singapore 117566, Singapore * Author to whom correspondence should be addressed.
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