# Improved SE-ResNet Acoustic-Vibration Fusion for Rolling Bearing Composite Fault Diagnosis
## 1 Introduction  
Bearings have widespread applications across various mechanical equipment fields. Ensuring their stable operation serves as a crucial prerequisite for the smooth functioning of such equipment [1]. The study of fault diagnosis methods has been a popular topic of research [2; 3; 4]. With the advent of the era of combining the machinery industry with big data, deep learning is presently one of the most intelligent features and cutting-edge research fields in fault diagnosis. The heart of intelligent fault diagnosis lies in the efficient acquisition, transmission, processing, reformation, and utilization of diagnostic information [5; 6; 7]. In turn, it can achieve the ability to accurately perform feature recognition and prediction of diagnostic objects in a given environment. For instance, convolutional neural networks (CNNs), generative adversarial networks (GANs), recurrent neural networks (RNNs), and auto encoders have been developed for fault diagnosis applications. CNNs are primarily employed forgrid-like structured data, including images and speech, whereas auto encoders are predominantly utilized for unsupervised learning tasks such as data dimensionality reduction and feature extraction [8; 9; 10]. Notably, CNNs have garnered significant research attention. Numerous scholars have utilized them in rolling bearing fault diagnosis, achieving remarkable results. [PERSON] et al. [11] proposed a wavelet transform and improved RNN based on the new pooling layer for dimensionality reduction of the feature map. Singular value adaptive decomposition is used for feature extraction. This method was verified on two bearing datasets for feasibility and robustness. [PERSON] et al. [12] proposed a combination of a GAN and short-time Fourier transform. The signal undergoes a transformation into a time-frequency map via the short-time Fourier transform (STFT), serving as the input for the classification GAN. Subsequently, the classification of bearings for fault diagnosis was achieved using the bearing dataset from Case Western Reserve University. [PERSON] et al. [13] proposed a hybrid CNN model with good robustness. This method involves combining CNN with random forest. The CNN is employed to autonomously extract features from the grayscale map of the input signal. Subsequently, a random forest algorithm is applied for fault classification. The experimental outcomes demonstrated that the model attained a diagnostic accuracy of 99.548% on the high-speed bearing dataset provided by Case Western Reserve University. [PERSON] et al. [14] utilized a continuous wavelet transform to convert the bearing vibration data into time-frequency maps. Advanced features are extracted by constructing a CNN with a square pooling structure. The extreme learning machine serves as a robust classifier. The dataset of motor bearings served to validate the effectiveness of the proposed method. [PERSON] et al. [15] constructed a spectrogram from the raw data through the application of the short-time Fourier transform. Then, it was sent to the CNN for fault classification. High accuracy was achieved. The above studies indicate that neural networks can be effectively integrated with other methods. Numerous scholars have conducted pertinent research exploring the integration of neural networks with other methodologies for fault diagnosis in distinct operational environments. This further enhances the application value of neural networks. [PERSON] et al. [16] integrated deep CNNs with gated recurrent units for bearing fault diagnosis. The raw signals, without preprocessing, are fed into the deep CNN, and its outputs are subsequently input to the GRU. This approach effectively diagnoses faults in rolling bearings. As another example, [PERSON] et al. [17] introduced an intelligent bearing fault diagnosis method that combines adaptive variational mode decomposition (AVMD), a deep belief network (DBN) based on mode ordering, and an extreme learning machine (ELM).