## 1 Introduction  
Sliding bearing is widely used in chemical, metallurgical, steel, and aerospace industries because of its smooth operation, high speed and impact property [1]. However, it is prone to damage due to harsh working environment, which can cause great catastrophes. Therefore, accurate fault diagnosis techniques are necessary to ensure the stable operation of equipment and avoid major safety accidents. It mainly includes theoretical research and condition monitoring for the sliding bearing fault diagnosis. The former is a classical means to perform the sliding bearing fault diagnosis, such as the mass-conserving boundary condition [2] which can effectively overcome the drawbacks of Reynolds equation [3] to perform fault diagnosis for sliding bearing. However, the problem of film cavitation in this technology is mainly hindrance in numerical calculation based on mass conservation of boundary conditions, which cannot meet requirements of modern industry. Therefore, condition monitoring is a useful and feasible method to perform bearing fault diagnosis [4]. The condition monitoring is mainly divided into vibration signal analysis, oil analysis, and acoustic emission signal analysis. Although oil analysis and acoustic emission signal analysis have unique advantages for strong background noise and early bearing failures, their application are limited due to expensive price. Therefore, vibration signal analysis plays an important role in bearing fault diagnosis, which is mainly divided into two parts, including time domain analysis [5; 6] and frequency domain analysis [7]. However, the time domain analysis can only roughly reflect whether the mechanical equipment is normal or not and cannot provide detailed information (fault type, fault location, and fault severity) about the bearing. It can generally be utilized to make simple diagnosis for the equipment. Thus, many studies contribute to utilizing frequency domain technology to identify spectrum structure. Especially in precise diagnosis, the sensitive fault information can be extracted to distinguish the anomaly type [8]. Although the fault feature can well represent the state of the sliding bearing based on above methods, it largely depends on the rich experiments and professional knowledge. It is not only inefficient, but also the accuracy is difficult to guarantee when the worker observes a large amount of data for a long time. Therefore, automatic identification has an extremely important significance for improving the efficiency and reliability of rotating machinery. Furthermore, the traditional methods have drawbacks that can lead to the reduction and loss of signal detail information, which cannot precisely distinguish several faults more efficiently according to the features of various faults [9]. Meanwhile, prior knowledge is more important for those methods and need to be known beforehand. However, it is difficult to get theses precious information for the practical sliding bearing, such as the unbalance, oil whirl, and oil whip in a mechanical system [10]. Therefore, machine learning [11]--such as support vector machine (SVM) [12], back propagation neural network (BPNN) [13], and decision tree [14] has an extremely important significance for improving the efficiency and reliability of the fault diagnosis for sliding bearing. An adaptive neuro-fuzzy inference system [15], which combined multi-scale entropy and adaptive neuro-fuzzy inference system, cannot only reliably discriminate among different fault categories and identify the level of fault severity, but overcome the limitations of traditional data-based models/techniques. Although the reported research has achieved high-precision intelligent diagnosis, it still needs strict requirements for complicated signal processing methods to extract accurate fault characteristics. Moreover, the raw signal is contaminated by intensive background noise that is deceptive to machine learning, reducing accuracy [16].