## 2 Related Research Work  
With the improvement of computer processing ability, especially the outstanding advantages of deep learning in feature extraction and pattern recognition, the application of deep learning to solve multi-view information fusion is a current research hotspot [8]. Generally speaking, multi-view information fusion based on deep learning can be divided into three levels, namely data level fusion, feature level fusion, and decision level fusion. 1. Data level fusion: [PERSON] et al. [10] connected the vibration signal, acoustic signal, current signal, and instantaneous angular velocity signal into a single channel signal, and used a convolutional neural network model (CNN) to learn the common feature space. [PERSON] et al. [11] combined the vibration signals from multiple views in parallel to form a two-dimensional matrix as the input of a CNN model to identify the faults of rolling bearings and gearboxes. The above multi-view information fusion algorithms mainly use serial or parallel methods to combine multiple-view data into single-view data, and then use a deep learning model for learning. Thereare differences in importance of multi-view data, and even some data may lead to misdiagnosis. Therefore, simply splicing multi-view data is not enough, and it will also exacerbate the occurrence of \"dimension disaster\", and an effective fusion mechanism is required to obtain better performance. 2. Feature level fusion: [PERSON] et al. [12] used a fast Fourier transform (FFT) to obtain the original spectrum of the current signal, and combined the original spectrum from multiple views into a two-dimensional matrix as the input of a CNN model to diagnose the fault of a gearbox. [PERSON] et al. [13] obtained fifteen time-domain features and three frequency-domain features by using time-domain and frequency-domain signal processing methods for vibration signals from three views. These features are then deeply feature extracted and fused using a sparse autoencoder (SAE). Finally, the fused features were used to train a deep belief network (DBN) for fault identification. [PERSON] et al. [14] obtained the time-frequency distribution images of vibration signals and current signals through a continuous wavelet transform, and then used the CNN model to capture the time-frequency image features and fuse them to achieve rotating machinery fault diagnosis. [PERSON] et al. [15] used an empirical mode decomposition method and CNN model to fuse the shallow and deep features of single view data for rotating machinery fault diagnosis. Our previous studies [16; 17] were based on deep learning to achieve multi-view feature level information fusion. In the literature [16], an ensemble model is proposed based on a CNN model, which extracts different features from single-view data and fuses them for bearing fault diagnosis. A fault diagnosis method based on multi-scale permutation entropy (MPE) and a CNN model is proposed in the literature [17]. First, features are extracted from multi-view data using MPE. Then a multi-channel fused convolutional neural network model (MCFCNN) is constructed, and the extracted MPE feature set is used as the model input for diagnosing gearbox and rolling bearing faults. The above methods first compress high-dimensional datasets into small-scale datasets with representative information, and then the deep learning model is used for learning and fault identification. The whole process does not consider the weight of different views and lacks generalization. For different diagnostic objects, different representative features need to be extracted. 3. Decision level fusion: [PERSON] et al. [18] proposed a model IDSCNN based on the CNN model and the improved D-S algorithm to achieve decision-level fusion of two views for bearing fault diagnosis.