## 1 Introduction  
Rotating machinery is the most critical component in mechanical equipment, and it is widely used in industrial production equipment [1]. Since rolling bearings are one of the most common structures in rotating machinery, the slight defects of rolling bearings may lead to the failure of the whole system and cause severe financial losses [2]. Therefore, it is of great significance to study the fault diagnosis of rolling bearings [3; 4; 5; 6; 7]. In view of bearing fault diagnosis, after a long period of exploration and research, many scholars have proposed various model-based methods [8]. Feature extraction and recognition [9] are the two most important processes in a diagnostic model. Generally, the main purpose of feature extraction is to extract the representative features, including the time domain features (kurtosis [10], crest factor [11], characteristic waveform [12], etc. ), frequency domain features (Fourier transform [13], etc. ), and time-frequency domain features (wavelet packet [14], empirical mode decomposition (EMD) [15; 16], etc.). Then, the extracted features are entered into classification algorithms, such as support vector machines (SVM) [17] and artificial neural networks (ANN) [18]. As for the fault diagnosis of bearings, [PERSON] et al. [19] applied the EMD to extract the time-frequency domain features, and a back propagation (BP) neural network to identify the faults. [PERSON] et al. [20]proposed a novel model based on the Hilbert-Huang transform (HHT) and ANN, and obtained good performance in fault diagnosis. [PERSON] et al. [21] suggested a bearing condition monitoring method based on EMD and SVM, which improved the accuracy of diagnosis. However, there are still some drawbacks to the fault diagnosis methods mentioned above: (1) Statistical parameters need to be extracted manually, and the professionalism of signal analysis and the importance of prior knowledge lead to poor model versatility to a large extent; (2) feature extraction process can result in loss of information; and (3) the classification model is shallow, so it is difficult to meet the requirements for diagnostic accuracy and robustness of complex mechanical systems. Therefore, an adaptive extraction and classification of features and reduction of the reliance on expert knowledge have gradually become a research hotspot in the fault diagnosis field. Many fault diagnosis models based on deep learning, which made full use of the advantages of deep network with the internal characteristics of automatic learning data, have been proposed [22; 23; 24; 25; 26]. [PERSON] et al. [24] proposed a model based on a deep neural network (DNN), which overcame the shortcomings of the traditional diagnostic models and verified the effectiveness of DNN in the fault diagnosis field by using the data sets of rolling bearings and planetary gearboxes. A convolutional Neural Network (CNN), as a typical deep learning method, is widely used in the field of pattern recognition. [PERSON] et al. [27] suggested an intelligent diagnosis method for transmission line based on a CNN, which improved fault classification accuracy. Compared with other deep learning algorithms, CNNs have three advantages: Local feeling field, weight sharing, and pooling, which greatly reduce network complexity and risk of over-fitting. The input data of a CNN model can be divided into two categories, processed data and raw data. By processing of the original data, Fourier transform data [28], envelope spectrum data [29], and wavelet packet data [30; 31] can be used as input data of CNN model. Although these models can achieve effective classification results, there are still some drawbacks: (1) Signal processing can result in information loss that can have an impact on diagnostic results, making an adaptive feature extraction function of CNN model not fully utilized [32; 33]; and (2) the choice of a signal processing method has a great influence on diagnosis results. Therefore, the present trend is to use raw data as an input data of a CNN model. [PERSON] et al.