## 1 Introduction  
Approximately 25% of total friction losses in machines are caused by bearings, especially in certain high-power internal combustion engines, where the proportion of the total loss may reach as high as 40% [1]. When a friction pair is severely worn, a series of serious effects will occur, which will affect the economy, power, and reliability of the internal combustion engine. According to statistics on ship claims from the Swedish Club (2010-2016), in main marine engines, compensations for main bearing failure account for 33.5% of total compensations, with an average compensation of 1.6 million US dollars per claim. In addition, the number of compensations increased by 110% across the data period, now ranking first among single causes of compensation across all part failures [2]. Thus, research analyzing and monitoring the lubrication conditions of these bearings is of great significance. Nowadays, many scholars have studied the hydrodynamic lubrication of journal bearings. Recently, many research studies on the two aspects of simulation analysis of bearing lubrication conditions and bearing condition monitoring have been published. For simulation analysis of bearing lubrication conditions, [PERSON] et al. presented a flexible CFD modelling approach for the prediction of temperature in a journal bearing. The results showed that circumferential variations in shaft temperature are localized close to the shaft surface and are not significant for bearing performance when the shaft position is steady [3]. Then, taking into account the time dependence of temperature in lubricant film, [PERSON] predicted the thermal design problem for a generic two-axial groove bearing [4]. However, these studies are steady-state analyses of bearing lubrication that did not consider the influence of external load changes. [PERSON] et al. considered the influence of dynamics in their CFD model and analyzed the power loss and minimum film thickness at different crank angles [5]. However, the effect of surface roughness was not taken into account in the studies above. In order to ensure the accuracy of the calculation, the effect of surface roughness on asperity contact is considered in the calculation model in this paper. For bearing condition monitoring, the traditional methods for testing or monitoring the lubrication conditions of bearings include the temperature method, the vibration method, and the oil analysis method. Some researchers have analyzed bearing temperature data to generate bearing fault warnings. [PERSON] et al. proposed analyzing the evolution of temperature in a bearing to know whether the temperature increased because of a fault or due to higher load [6]. [PERSON] et al. used a neural network model to analyze bearing temperature data and evaluate the lubrication conditions of bearings [7]. [PERSON] et al. used the temperature signals to diagnose motor bearings [8]. However, because of the thermal inertia of the temperature sensors, their response speed is not as good as that of the vibration method. Other researchers used the vibration method to monitor the lubrication conditions of the bearings, such as [PERSON], who used the vibration method to diagnose motor bearing faults [9]; [PERSON], who used vibration signals to diagnose faults in big end bearings [10]; [PERSON], who analyzed the vibration characteristics of different clearances of bearings to monitor the lubrication of bearings [11]; and [PERSON], who proposed a method to monitor bearing failure based on vibration [12]. However, the vibration method is difficult to apply due to the many excitation sources, complex transmission paths, signal interferences, and low signal-to-noise ratio. Some researchers used the oil analysis method to monitor the conditions of bearings, such as [PERSON], who used ferrography and spectrometric analysis technology to monitor the conditions of bearings [13], and [PERSON], who described a method to monitor main bearing health using periodically collected lube oil samples [14].