)1 College of Mechanical Engineering, Xinjiang University, Urumqi 830017, China; [EMAIL_ADDRESS] (Z.J. )[EMAIL_ADDRESS] (Y.T. )1 College of Software, Xinjiang University, Urumqi 830091, China; [EMAIL_ADDRESS] (Y.L. )1 College of Mechanical Engineering, Xinjiang University, Urumqi 830017, China; [EMAIL_ADDRESS] (Z.J. )[EMAIL_ADDRESS] (Y.T. )[EMAIL_ADDRESS] (Y.T. )[EMAIL_ADDRESS] (Y.T. )1 College of Software, Xinjiang University, Urumqi 830091, China; [EMAIL_ADDRESS] (Y.L. )[EMAIL_ADDRESS] (Y.L. )[EMAIL_ADDRESS] (Z.J. )[EMAIL_ADDRESS] (Y.T. )[EMAIL_ADDRESS] (Y.T.)[EMAIL_ADDRESS]. With the advent of computer technology, digital signal processing has become integral to bearing fault diagnosis. Initially, traditional data-driven fault diagnosis methods encompassed three key steps: feature extraction, selection, and classification. In extracting features, it often converts the collected time domain data into frequency domain or time-frequency domain data through mathematics to obtain valuable features. The Fast Fourier Transform (FFT) [5] is a typical example of this type of transformation method, while the wavelet transform (WT) [6], the Hilbert transform (HT), and its variants [7] are commonly used to generate time-frequency domain data from time domain data. In the feature selection phase, it aims to preprocess the dataset before sending it to the classifier to select its features further. Classical algorithms include Binary Grey Wolf Optimization (BGWO) [8], Ant Colony Optimization (ACO) [9], binary particle Swarm optimization, and other methods [10]. In the classification stage, classical algorithms include machine learning (ML) [11] methods such as Support Vector Machine (SVM) [12], Random Forest (RF) [11; 13], and K-Nearest Neighbor (KNN) [14]. Traditional fault diagnosis methodologies often grapple with limited model generalization, challenging human interaction, and extensive workload when confronted with voluminous data. In response to these challenges and spurred by the big data revolution, scholars are increasingly adopting deep learning (DL) [15] techniques for rotating machinery fault diagnosis. Deep learning excels in robust feature extraction, automates data representation, and handles vast datasets with notable flexibility. For example, [PERSON] et al. converted the original vibration signal into a two-dimensional time-frequency image (TF-image) through a differential continuous wavelet transform [16]. They used this image as the network input to realize the gear fault diagnosis. The advantages of Graph Convolutional Neural Networks are also apparent. Images can provide rich spatial and frequency information [17], and more in-depth research can be done. However, the original signal can be converted into an image; this adds to the cost of pretreatment and calculation and could introduce errors in the preprocessing step. Compared with processing images, the one-dimensional deep learning model can be directly trained using the original data without additional transformation processing, and its computational cost is smaller and more lightweight [18]. The advantages of the one-dimensional deep learning model are also more suitable for the practical application of fault diagnosis. Recently, extensive research has been conducted on one-dimensional deep learning for fault diagnosis. [PERSON] et al. developed a model combining a Wavelet Convolutional Neural Network (WCNN) with a Bidirectional Gated Recurrent Unit (BiGRU) named WCNN-BiGRU, which achieved over 99% diagnostic accuracy [19]. However, the model's resistance to noise has yet to be tested. [PERSON] et al. proposed a new intelligent fault diagnosis model (IDCNN-GRU) combining improved Deep Convolutional Neural Networks and Gated Recurrent Units [20], and the fault recognition accuracy of this model reached 97.9% under the interference of multiple loads and intense noise. Furthermore, researchers have successfully integrated the attention mechanism into the traditional deep learning framework to address model limitations and enhance holistic feature extraction.