## 1 Introduction
The feature information that is beneficial to classification can be effectively extracted through the two stages of signal acquisition and feature extraction, and then, the extracted feature information can be input into various classifiers, such as support vector machine [17], k-nearest neighbor (KNN) [18], and back propagation neural network [19] to realize the fault classification and recognition of rotating machinery. In recent years, with the development of signal processing, pattern recognition, and artificial intelligence, deep learning has been widely used in image, speech recognition, and other fields. Deep learning has a strong ability of automatic feature extraction, which shows unique advantages and great potential in feature extraction and pattern recognition [20]. The intelligent fault diagnosis method based on deep learning has gradually become a research hotspot in the field of fault diagnosis, which can not only extract fault features adaptively but also process the original fault signal directly. As one of the classical algorithms of deep learning, convolution neural network is widely used in the field of intelligent fault diagnosis. Many scholars try to convert the original data or one-dimensional original data into two-dimensional image data through some methods as the input of convolutional neural network to complete the feature extraction and classification of faults. The neural network fault diagnosis method based on LeNet-5 converts a one-dimensional signal into two-dimensional image data as the input of convolution neural network, effectively extracts the image features, and eliminates the influence caused by manual extraction of the signal [21]. The core idea of the bearing fault diagnosis method based on the combination of short-time Fourier transform and CNN is to convert the original signal into two-dimensional image data suitable for CNN processing through short-time Fourier transform [22]. A multiscale kernel residual convolution neural network (CNN) is applied to motor fault diagnosis. By applying a multiscale kernel algorithm in CNN architecture and embedding residual learning into a multiscale kernel CNN, the difficulty of motor fault diagnosis caused by the high complexity of vibration signals is effectively solved [23]. Combining 1 DCNN with self-normalized neural network SNN, making use of the self-normalization characteristic of SELU activation function, an \(\alpha\)-Droting layer is introduced twice to regularize the training process, and the problem of over-fitting of the model is solved successfully [24]. The new fault diagnosis method of cyclic spectral correlation (CSCoh) and convolutional neural network (CNN) estimates the two-dimensional CSCoh diagram of the original vibration signal through cyclic spectral analysis and has a specific fault-type azimuth identification method, which effectively improves the fault identification performance of rolling bearing [25]. Combining the automatically extracted features with manual features, deep twin convolutional neural networks with multi-domain inputs (DTCNNMI) for diesel engine misfire fault diagnosis is proposed, which effectively improves the accuracy and robustness of the model under strong environmental noise and different working conditions [26]. Support vector machine (SVM) is integrated into deep convolution neural network (DCNN), and a DCNN-SVM network model is proposed, which improves the accuracy of fault classification and recognition, and the convergence speed and generalization ability of the model are also significantly improved [27]. The fault diagnosis method of rolling bearing is based on the order tracking algorithm and 1 DCNN (OT\(-\)DCNN). By resampling the data at different rotational speeds and then extracting and classifying the fault data, the difficult problem of fault feature extraction of rolling bearing under variable speed conditions is solved, and the fault diagnosis of rolling bearing under variable conditions is realized [28].