# Research on Intelligent Fault Diagnosis of Rolling Bearing Based on Improved Deep Residual Network
## 1 Introduction
presented a novel method using Convolutional Neural Networks and the Stochastic for detecting early bearing defects under changeable operating speeds [24]. [PERSON], [PERSON], et al. developed a new approach based on convolutional neural network (CNN) and multiple sensors for fault diagnosis of rotating machinery [25]. [PERSON] et al. proposed a new DL model according to a dilated convolutional neural network (D-CNN) used for bearing faults detection in induction motors (IMs) [26]. [PERSON], [PERSON], et al. put forward method that was applied to recognize faults of the centrifugal pump using advanced convolution neural network (ACNN) and acoustic images [27]. [PERSON], [PERSON], et al. combine support vector machine (SVM) and convolutional neural network to propose a new hybrid intelligent fault diagnosis frame which is better than some traditional fault diagnosis methods and has high precision for rolling bearings [28]. [PERSON], [PERSON], et al. applied the Optimized Deep Belief Networks with Improved Logistic Sigmoid Units for Planetary Gebaroxes of Wind Turbines [29]. [PERSON], [PERSON], et al. developed a LiftingNet which achieved layer wise feature learning and effectively classify mechanical failure data even with different speeds and under the effects of random noise [30]. [PERSON] et al. combine convolutional neural networks and nonlinear auto-regression neural networks to put forward a new method for imbalanced fault diagnosis of the rotating machinery [31]. [PERSON], [PERSON], et al. put forward a method based on motor current signature analysis and 2-D convolutional neural network used for gearbox fault diagnosis [32]. [PERSON], [PERSON], et al. presented a new convolutional neural network with a one-dimensional structure (ODCNN) for the automatical fault diagnosis of rolling bearings [33]. However, these methods have too many parameters, and the network convergence speed is slow and cannot be used to practical projects. To solve this issue, this paper developed an improved deep residual network for developing an intelligent fault diagnosis system. By changing the deep residual network structure, the network training time can be shortened, and the fault recognition accuracy can be improved. The novel method can extract features from the raw vibration signal and avoid manual feature extraction. The method is more reliable and effective than traditional fault diagnosis methods. Based on the excellent performance of the improved deep residual network, the rolling element bearing fault detection technology in a one-dimensional vibration signal is studied. Also, from the experimental results, it is validated that our proposed ResNet model is effective. To summarize, our contributions as following:In this paper, the improved deep residual network structure is proposed, and GAP is introduced to replace the full connection layer. It is an effective method to solve the problem of too many parameters in traditional deep residual network model. The method is validated on rolling bearing fault data of different types. Compared with existing models, the training time of this model is shortened, and the classification accuracy is higher. The rest of arrangement of this article is as follows. Section 2 presents the background of ResNet. In Section 3, the particulars of the proposed fault diagnosis method are presented. Section 4 presents one comprehensive case study of fault diagnosis for rolling element bearings to illustrate the effectiveness of the presented method. Section 5 is the summary of paper.