## 1 Introduction  
Rolling bearings are the key components of various rotating machinery that are widely used in all walks of life [1; 2]. The failure of the bearings could lead to serious consequences such as grave safety accidents, long breaks of production and great pecuniary losses. Thus, accurate status monitoring and timely fault identification of REB is significant for the safe and regular operation of large machinery [3; 4]. Accordingly, research and development in REB fault diagnosis has had an all-important social significance and economic value [5; 6; 7]. In recent years, the researches on rolling bearing fault diagnosis mainly focused on two kinds of methods [7]: (1) Fault diagnosis based on feature extraction and fault recognition; (2) Self-learning and self-evolution fault diagnosis based on deep learning. The first kind of methods firstly obtained fault symptoms from fault signals by signal analysis methods, and then identified fault types through fault recognition methods. Feature extraction [8; 9; 10] and fault recognition are two determinant steps which affected the accuracy of fault diagnosis. Therefore, the related researches mainly focused on feature extraction method [11; 12] and fault recognition method. The second kind of methods [13] did not need advance feature extraction and could learn complex fault parameters from the original fault data and realize fault identification autonomously. The related researches mainly focused on deep neural network and various deep learning methods based on it. Signal processing is the most effective and widely used method for fault feature extraction and is the significant basis for machinery fault diagnosis. Recently, many kinds of signal analysis methods have been widely used in fault diagnosis, such as wavelet [14; 15], empirical mode decomposition (EMD) [16; 17], variational mode decomposition (VMD) [18; 19] and singular value decomposition (SVD) [20; 21]. They are the most important signal analysis methods in REB fault diagnosis because of their prominent effectiveness. After a long time of in-depth research by a large number of scientists, these signal analysis methods all could obtain good enough fault feature of rolling bearings. In addition, intelligent algorithms are another key technology for fault diagnosis, and a variety of such algorithms [9; 22; 23; 24], such as support vector machine [9; 22], clustering algorithms [23], neural networks [24] and so on, are used to design and improve the intelligent fault diagnosis system. Signal processing and an intelligent classification algorithm cooperate efficiently in REB fault diagnosis. With the capacity of automatically learning complex features of input data, deep learning architectures have great potential to overcome drawbacks of traditional intelligent fault diagnosis. Accordingly, deep learning algorithms have been applied widely in machine health monitoring recently [2]. [PERSON] [2] proposed a method for diagnosing bearing faults based on a deep structure of convolutional neural network, and this method has high accuracy and robustness in noisy environment. With deep convolutional neural network as the main structure, [PERSON] [13] proposed a novel domain adaptation method for rolling bearing fault diagnosis. It minimized the maximum mean deviation between the source domain and the target domain in multi-core structure, and significantly improved the performance of cross-domain testing. An integrated deep learning diagnosis method based on multi-objective optimization is proposed by [PERSON] [5]. This method weighted and integrated convolutional residual network (CRN), belief network (DBN) and depth automatic encoder (DAE) to realize effective diagnosis of bearing faults. [PERSON] [6] presented a capsule network with an inception block and a regression branch, which improved the generalization ability of deep neural network.