## 1 Introduction
Such data are vulnerable to external factors, such as sensor malfunctions and strong noise interference, which can negatively affect the accuracy of diagnostic models. To address this limitation, a signal decomposition method called singular spectral decomposition (SSD) is proposed with the help of singular spectral analysis (SSA) [25], and it has excellent resistance to noise interference. Therefore, it is meaningful and necessary to use this method to separate the strong interference background from weak fault signals for the fault diagnosis of rolling bearings. Moreover, rotating machinery is a complex system composed of multiple components, typically monitored by multiple sensors simultaneously. Therefore, utilizing multimodal sensor data to build and train models is of great importance for improving the accuracy of equipment fault diagnosis [26]. This paper focuses on the bearing gearbox as the research object and introduces a multi-modal fusion model and decision fusion strategy. By combining various signal modalities, such as vibration, acoustic, and electrical signals, we conduct a comprehensive analysis aimed at investigating fault diagnosis techniques based on multi-modal signals. We have established effective fusion strategies for multiple modalities, including vibration, acoustic, and current signals, fully utilizing the correlations among these multi-modal signals. Through feature decision strategies, our model not only accurately identifies single-modal features but also learns the interactions between different modalities, thereby improving the accuracy of fault diagnosis while achieving efficient fault classification. To address the limitations of using single-modality data for diagnosing various faults and the potential errors that arise from relying solely on one modality, this study proposes an advanced deep learning fault diagnosis framework called multimodal fusion fault detection (MFFD). The MFFD framework is based on multimodal decision fusion and aims to enhance diagnostic accuracy by integrating information from multiple sensor modalities. First, SSA is employed to extract features from the data. During the multimodal data fusion process, particular emphasis is placed on decision-level fusion. In this process, different deep learning models are optimized for each modality to generate prediction results, and a weighted average fusion strategy is applied to combine the feature outputs from different modalities. Compared to single-modality data, this multimodal fusion-based detection model effectively overcomes the limitation of single-modality approaches, which may fail to detect certain types of faults. Its contributions are summarized as follows:  
1. To better represent the fault-related information of vibration and signals, singular spectrum time series analysis is used to extract signal features. 2. A novel network is constructed using recurrent gated convolution to learn and optimize two-dimensional image data. 3. The decision-level fusion method integrates different deep learning models to achieve more accurate fault diagnosis results. 4. We conducted experimental evaluations of the proposed method on a dataset of bearings with various faults and performed a comprehensive comparative study.