## 2 Related Work  
Currently, research on fusion methods based on multimodal signals is divided into three main types [27]: data-level fusion, feature-level fusion, and decision-level fusion. The strategy of data-level fusion is to integrate raw data from multiple modalities into a complete dataset using signal processing techniques. This approach usually involves simply splicing or combining data from different modalities through different channels to form fused data [28]. For example, [PERSON] et al. [29] and [PERSON] et al. [30] fused acoustic and vibration signals for gearbox fault diagnosis and demonstrated that multi-sensor fusion performs better than single-sensor based methods. The significant advantage of this strategy is the ability to fully utilize the information content of the original data in order to minimize the loss of information during the fusion stage. However, most of the existing data-level fusion techniques simply splice or combine the data collected by different sensors through weighted averaging, which may suffer from insufficient interpretability in interpreting the processing of each sensor's data.  
Feature-level fusion methods first extract signal features from multiple sensors through signal analysis or neural networks and then fuse them by means of feature combination and an attention mechanism to enhance robustness. The multi-layer deep fusion network model proposed by [PERSON] achieves adaptive hierarchical fusion by fusing features at different levels through branching and central network structures [31]. [PERSON] proposed a multi-scale, multi-sensor fusion network based on deep learning for motor fault diagnosis [27]. However, feature-level fusion strategies have drawbacks, such as the fact that signal feature acquisition methods that rely on expert experience may limit the robustness and information content of the fused features, and deep learning-based methods may result in missing information by focusing on a limited feature depth.  
In implementing a decision-level fusion strategy, the process of information fusion involves integrating decision outcomes from different modal data fusion methods, typically including strategies such as the majority voting fusion strategy, algorithms based on Dempster-Shafer (DS) evidence theory, and their improved versions [32]. These methods exhibit high real-time performance and robustness. Additionally, some researchers have employed a stacked autoencoder using the Morlet wavelet function to extract key features from a series of sensor data and perform classification on these features. To address key issues in multimodal decision fusion, a flexible weighted distribution fusion strategy has been designed, which effectively integrates decision information from different modalities [32]. Given the distinct characteristics of vibration signals and infrared images, [PERSON] et al. [33] proposed a fusion framework based on confidence-weighted support matrix machines, which utilizes DS evidence theory to fuse the posterior probability outputs of different sensor data. [PERSON] et al. [34] proposed a decision-level fusion method using vibration and current signals to identify the three health states of a gearbox. Furthermore, deep learning models have been applied to extract key features from various sensor data, with the decision-level fusion process being carried out using weighted DS evidence theory. Although decision-level fusion strategies demonstrate excellent performance in terms of noise resistance and practicality, their effectiveness largely depends on the fusion strategy employed [35].