## 1 Introduction
However, IFD has not yet been completely realized, and these ML models' decision-making capabilities still rely on the extraction and selection of defect statistical data. In response to some deficiencies of ML, DL-based techniques have advanced significantly in the last several years. Compared with ML, DL does not require human experience for feature engineering. Moreover, when dealing with substantial volumes of data, the model can usually demonstrate good performance, because abundant data enables the model to learn the underlying patterns more effectively while mitigating the risk of overfitting. [PERSON] et al. [23] proposed a bearing fault diagnosis model using a self-attention-based LSTM autoencoder with graph convolutional networks (GCNs), which addresses the issues of imbalanced fault data and data complexity. [PERSON] et al. [24] proposed a hybrid convolutional neural network (CNN-LSTM), and by employing quantile regression in the network output, the proposed method aims to manage the uncertainty present in the data. [PERSON] et al. [25] proposed a hybrid model combining long short-term memory (LSTM) networks, random forest (RF) classifiers and gray wolf optimization (GWO) algorithms, which significantly improved the accuracy and performance indicators of fault detection. [PERSON] [26] proposed a fault diagnosis method for bearing composite defects based on zero-shot learning. The experimental results showed that the proposed method achieved a high accuracy of 75.64% in diagnosing compound bearing faults. [PERSON] et al. [27] innovatively proposed a neural network for bearing fault diagnosis based on physical mechanisms. This new type of network shows higher accuracy in classifying bearing signals of different fault types. [PERSON] et al. [28] established a convolutional neural network with convolutional attention and multi-channel features. This method converts one-dimensional signals into two-dimensional time-frequency signals and adds Gaussian white noise, improving the model's accuracy. [PERSON] et al. [29] proposed a fault diagnostic technique intended to overcome the low-accuracy issues brought on by conventional models' inadequate generalization. Their method combined multi-level residual attention mechanisms with multi-scale sliding convolutional neural networks. Specifically, a multi-scale parallel convolution strategy was employed to enhance feature extraction. The model's diagnostic performance and capacity for generalization were greatly enhanced by this approach. Although the accuracy rate of FD by the above-mentioned methods is very high, these methods do not take into account the impact of signals in the frequency domain on FD. And the time-domain signals may contain noise, which will affect the results of FD. However, following the FFT's conversion of time-domain inputs into frequency-domain signals, the influence of noise will be reduced. There is a potential correlation between frequency-domain features and time-domain features (for example, some faults suddenly change in the time domain corresponding to high-frequency components in the frequency domain), and cross-attention can explicitly model this cross-domain dependency. [PERSON] et al. [30] proposed a novel self-supervised feature learning framework, namely the notch filter augmented multi-channel self-supervised learning (NFA-MSSL), which can extract torque-invariant and fault-related features by transferring frequency-domain knowledge to the time domain, thereby addressing the issue of significant data distribution differences. [PERSON] and [PERSON] [31] proposed an accurate and noise-resistant deep learning model for diagnosing bearing faults. They designed a time-frequency multi-domain fusion block and incorporated the physics of bearing faults into the model parameters, thereby improving the fault classification accuracy in noisy environments. [PERSON] et al.