## 1 Introduction
Furthermore, traditional machine learning will not be able to handle massive data to a certain extent, which will reduce the efficiency of diagnosis due to development of signal acquisition technology. Deep learning--including convolutional neural network (CNN) [17], deep neural network (DNN), [18] and SAE [19]--can effectively overcome deficiencies above in current intelligent diagnosis methods. It refers to multiple processing layers and simple structures to explore the hidden information of signal for the sliding bearing fault diagnosis. An improved SAE [20] is proposed to overcome fluctuations of the shaft speed, and it outperforms conventional fault diagnosis algorithms in terms of fault classification accuracy. It benefits from data mining ability of SAE to outperform traditional machine learning. However, it is also affected by intensive background noises that can make difficulties for learning the sensitive information to interference diagnostic accuracy. Therefore, the statistical filter performs the purification for the raw signal by removing the noise and giving prominence to the fault feature. Moreover, the fault of the sliding bearing--such as oil whirl, dynamic unbalance, static unbalance, and contact rubbing--are centralized in low frequency or mid-low frequency, with no obvious boundaries between unbalance faults. Therefore, stepwise intelligent diagnosis method based on SAE is proposed to solve problems above to improve the sliding bearing fault diagnosis. It also can overcome the disadvantage that fault feature health status cannot be extracted accurately with the theoretical methods in practice equipment diagnosis process. To sum up, the proposed method has the following novel benefits and solves corresponding problems. 1. The proposed method can automatically learn useful fault information with the deep structure of SAE, which cannot depend on rich experiments and professional knowledge to implement intelligent fault diagnosis. Moreover, the statistical filter in the proposed method can attenuate noise effects to extract the sensitive feature further, and it can sensitively reflect the symptoms of the equipment states. 2. The proposed method effectively distinguishes five kinds of conditions through the stepwise intelligent diagnosis with the SAE after filtering. Although the various faults similarities in sliding bearing are higher than others in rotor system, it can accurately and efficiently distinguish them in a large number of signals. 3. The detail relationship between fault feature and fault type in sliding bearing can be described with the data mining capabilities owning to SAE, which is not affected by the negative impact of the data dimension. Therefore, it can objectively and effectively improve the diagnosis accuracy and enhances the robustness of the proposed method. To indicate the feasibility and effectiveness of the proposed method, the paper presents detailed experiments for diagnose faults of sliding bearing including contact rubbing, oil whirl, dynamic unbalance, and static unbalance. The experimental results demonstrate that the proposed method can get high-precision fault diagnosis compared with other several methods.