## 1 Introduction
Furthermore, acoustic radiation signals have shown prominence in early fault detection. They exhibit high sensitivity to early-stage weak signals of micro-cracks or wear, enabling the capture and diagnosis of faults at their initial stages, thus achieving preventive maintenance. This further enhances the application potential of acoustic radiation signals in rolling bearing fault diagnosis. Acoustic radiation techniques have a long history in fault diagnosis and have proven effective in various industrial applications [13, 14, 15]. Relevant scholars have conducted the following research. [PERSON] et al. [16] proposed a new framework for bearing fault diagnosis based on a word segmentation network. This framework uses signal segmentation methods, fault pattern mapping, and density peak clustering methods to comprehensively model AE signals, enhancing fault detection capabilities under different rotational speed conditions. [PERSON] et al. [17] proposed a method for bearing fault diagnosis using acoustic radiation signals. By using normalized bearing characteristic components (NBCCs) as inputs to CNNs and by combining with Grad-CAM, they achieved feature extraction and visual interpretation. [PERSON] et al. [18] proposed a novel bearing fault diagnosis algorithm using acoustic signals. This method is based on deep extreme learning machine (SNP-DELM) and the efficient fault diagnosis of bearings using motor current signals. [PERSON] et al. [19] investigated the use of time-domain filtering and frequency-domain filtering, combined with a two-step Wiener filtering method, to achieve bearing fault signal enhancement and effective noise control. They used the parameter-adaptive MOMEMDA method to analyze the output of bearing fault signals, achieving bearing fault diagnosis and fault location identification. Although acoustic signals have certain advantages in non-contact measurement and early fault detection, extracting fault characteristic information of rolling bearings from complex noise signals remains a challenging issue. Therefore, this paper proposes a method to transform acoustic signals into one-dimensional and two-dimensional representations, combining them for feature fusion using ResNet. By integrating features from one-dimensional signals and two-dimensional time-frequency diagrams, this approach fully utilizes the complementarity of these two signal representations. One-dimensional signals can provide local temporal features, while two-dimensional time-frequency diagrams can provide global frequency characteristics. To further enhance the feature extraction and fault diagnosis capabilities of the model, this paper introduces the multi-head attention mechanism. The multi-head self-attention mechanism, as an important component of deep learning models, has gained wide application in the fields of natural language processing and computer vision [20; 21; 22]. Its development began with the Transformer model, which effectively addresses the long-distance dependency issues in sequential data processing using the attention mechanism. This significantly improves computational efficiency, making it suitable for large-scale data processing and complex model training. In recent years, the multi-head self-attention mechanism has shown a broad application prospect in the field of mechanical fault diagnosis. It can simultaneously focus on different feature channels, extracting more detailed and rich fault features, thereby improving diagnostic accuracy. When analyzing time series data, the multi-head self-attention mechanism can capture long-distance dependencies within the data, aiding in the identification of early fault signals. [PERSON] et al. [23] proposed a two-level ABD system for noise reduction and anti-noise fault diagnosis. This system uses adaptive multi-head self-attention mechanisms to suppress background noise in multiple channels.