## 1 Introduction  
Wind energy, as a kind of renewable and clean energy resource, has become the fastest-growing energy source [1]. Wind farms capture the wind flow through wind turbines (WT), which can convert the wind energy into electricity by rotating blades around a rotor. In the WT systems, regardless of on the main shaft or in the gearbox or the generator, rolling element bearings are critical mechanical components to support the shaft or the rotor [2]. Complex working conditions, such as time-varying rotating speed, nonstable load and harsh environments, challenge the reliability of WT units. This could easily lead to wear and tear on bearings, resulting in faults or even fatal breakdowns, causing catastrophic accident and economic losses [3; 4]. Investigation has shown that more than 50% of faults on gearboxes were caused by bearings [5]. Together with the various types and certain amounts, bearing faults bring difficulties to the maintenance of WT units. With the wide installation of wind turbines worldwide, the accurate condition monitoring of rolling element bearing has attracted considerable attention in recent years. Once a local bearing defect appears, it induces an impulse excitation to the system, which will excite resonant frequencies of the bearing and its adjacent components [6]. Thus, vibration signal is widely used in bearing fault diagnosis. A large volume of works has purely relied on the vibrational signals for fault detection and classification without consideration of the failure mechanisms lying behind. These methods are usually categorized as data-driven methods and many such methods have been developed [7; 8; 9]. However, these data-driven methods require a good knowledge of mechanical structure and vibration mechanism. On the other hand, it is the foundation of mechanical fault diagnosis to find out the mechanism and representation form of the fault [10; 11]. Through model-based analysis, dynamic characteristics of the system could be obtained. [PERSON] [12] developed a model to describe the vibrations produced in a rolling element bearing under constant radial loads with a single point defect on the inner race. [PERSON] et al. [13] proposed a new model taking time-varying contact stiffness into consideration, to reveal the relationship between vibration characteristics and defect sizes. Comparatively, few scholars have focused on the vibration characteristics and health monitoring of bearings before obvious local defects appearing on raceways. In a WT system, unsteady wind and gusts will lead to high contact stresses on rolling elements and bearing skidding. Wear is the main cause of bearing failures [14]. Especially, abrasive wear seems to be the most common wear type observed in bearings in the industry [15]. Investigation has shown that wear and tear can shorten bearing by about 30% [16]. However, during the lifetime of bearings, the wear process is different from surface roughness, and it is difficult to measure wear loss, which needs special equipment and methods [17]. Meanwhile, investigations show that wear and tear during the bearing operating process give rise to clearance [18]. In addition, the internal radial clearance affects the load distribution, and consequently influences the fatigue life of rolling bearings [19; 20]. In view of structural vibrations also highly influencing the propagation of wear [15], significant efforts need to be dedicated to study the vibration characteristics under different bearing clearances before obvious local defects appearing, which could provide more guidance for bearing condition monitoring. Tribology-focused techniques provide meaningful exploration for bearing fault diagnosis and condition monitoring. [PERSON] and [PERSON] [18] expounded the connections between bearing diagnosis and tribology mechanisms, pointing out that rolling contact wear and fatigue during rolling bearing operations can be diagnosed by combining measured and interpreted condition monitoring data. [PERSON] et al.