# Improved SE-ResNet Acoustic-Vibration Fusion for Rolling Bearing Composite Fault Diagnosis
## 1 Introduction
This method can adaptively decompose unsteady vibration signals into temporary frequency components, organize a set of effective frequency components, and facilitate online fault diagnosis. Additionally, [PERSON] et al. [18] proposed an enhanced CNN-based fault diagnosis approach for rotating machinery. With a reduced number of parameters, it can swiftly and precisely identify fault types, thereby enhancing the efficiency of fault diagnosis. [PERSON] et al. [19] explored a deep learning approach for bearing fault diagnosis, incorporating a CNN and LSTM; however, their study was limited to a specific fault type. Similarly, [PERSON] et al. [20], making use of multi-sensor fusion, utilized deep neural networks for bearing fault diagnosis, but their research also focused on only a single fault type. The literature on composite fault diagnosis remains limited compared to individual fault diagnosis. The majority of the aforementioned studies focus on single-fault scenarios. With the development and wide use of machinery industry, composite faults of rolling bearings appear frequently. [PERSON] et al. [21] introduced an intelligent diagnosis model that combines a CNN with a support vector machine, successfully diagnosing various composite faults between rolling bearings and rotors. Their results demonstrated the model's strong generalization capabilities in identifying different composite faults. Furthermore, [PERSON] et al. [22] enhanced the LeNet network by designing the LeNet-F network specifically for composite fault diagnosis. This model, validated on the K210 development board, achieved a composite fault classification accuracy of 97.41%. [PERSON] et al. [23] proposed a method for composite fault diagnosis in rolling bearings that employs multi-scale fuzzy entropy feature fusion. This approach improves the discriminatory power of composite fault features compared to traditional methods that are limited to extracting single fault feature information. [PERSON] et al. [24] introduced the enhanced periodic mode decomposition (EPMD) method, the experimental results of which indicate as an effective tool for diagnosing composite faults in rolling bearings. Through the analysis of existing literature, rolling bearings have been found to be widely utilized in various fields, such as machine tools, steel production, and electricity generation. They serve as crucial components of rotating machinery. Effective condition monitoring and fault diagnosis are essential for their maintenance. Accurate diagnosis of faults, particularly in their early stages, will contribute significantly to the stable operation of the equipment. Composite faults in bearings are more difficult to diagnose than individual faults. Limited research exists on the utilization of an acoustic-vibration combination for addressing composite fault problems. Some models have achieved high accuracy, but are complex. Some models are simpler, but have only achieved average accuracy. Deep learning methods for diagnosing composite faults often require deeper network structures. In this article, effective noise reduction processing is conducted before fault diagnosis. This ensures precision without adding undue complexity to the network. The key aspects of this paper are as follows: Firstly, an enhanced EEMD technique, incorporating GS-SVR alongside a window function, is introduced to reduce noise in rolling bearing vibration signals. Subsequently, MTFs are employed to transform one-dimensional signal data into two-dimensional images post-noise reduction. Next, a rolling bearing composite fault diagnosis approach that makes use of the SE attention mechanism and ResNet network is proposed. Lastly, comparative analysis with alternative network structures validates the stability of the enhanced SE-ResNet, satisfying the anticipated requirements for composite fault detection.