## 1 Introduction  
Marine diesel engines must endure continuously heavy loads; therefore, it is inevitable that they will experience increased frictional power consumption and wear. The frictional power loss caused by the bearing account for about 25% of total friction loss [1]. The main bearing of a marine diesel engine is subjected to cyclical alternating loads, and the main bearing is prone to excessive wear and axle holding failures, which seriously affect the safe operation of a vessel. On-line monitoring of wear on the main bearing of the engine would greatly improve reliability for a vessel. Current methods for on-line monitoring include temperature monitoring, oil mist detection, the vibration method and oil analysis. The temperature method needs to perforate the back of the bearing [2, 3]; the oil mist detection [4] and vibration methods [5, 6, 7, 8, 9] are not sensitive to failures; and the oil analysis technology [10] can only be used for monitoring the overall wear of the engine, but it cannot determine the specific worn part. Recently, more and more attention has been paid to deep-learning-based approaches with automatic feature learning capability. [PERSON], [PERSON] et al. built a new framework for rotor-bearing system fault diagnosis by using CNN with transfer learning [11]. [PERSON] et al. built a data driven model by Grey-box modelling, for studying the swirl characteristics in a gas turbine combustion system [12]. [PERSON], [PERSON] et al. used PSVM to build a fault diagnosis system. The results show the effectiveness of the extracted features from the acquired signals in the diagnosis of the machine condition [13]. However, this approach needs to be based on sensitive signals. Therefore, these methods have certain limitations. The CV methods are used to monitor the CV potential between the main bearing and the crankshaft. The main bearing and the main journal generate frictional heat due to hydrodynamic lubrication, and the generated heat makes more active free electrons on the main journal flow to the ground by the tunnel effect [14]. Therefore, there is a voltage between the crankshaft and the ground. A CV sensor is used to measure CV signals caused by wear of the main bearing, and this is analysed by extract characteristics to support on-line monitoring of the wear status of the main bearing. [PERSON] et al. compared the calculated oil film thickness with the test CV signal and proposed that the CV signal can reflect the oil film thickness of the bearing [15]. [PERSON] et al. took a four-cylinder diesel engine and tested the relationship between the bearing temperature, the axis orbit, and the contact potential, then verified the effectiveness of the contact potential for monitoring the bearing wear status [16]. [PERSON] et al. designed a CV sensor and verified its potential contact signal with good repeatability, and then confirmed the effectiveness of the pyroelectric method in monitoring wear of the main bearing in marine diesel engines [17]. The wear characteristics of the main bearings of marine diesel engines are not only related to the inherent characteristics of the materials, but also relate to the characteristics of the mechanical system. This involves many factors, such as mechanical load, speed, working environment and properties of the materials. Therefore, a single factor friction test is required to analyse the influence of different working conditions on the characteristics of the bearing wear. [PERSON] et al. used a rotor test bench to verify whether the CV methods could be used to evaluate the impact of bearing coatings on the performance of bearing lubrication, and proposed that the contact potential was more sensitive to changes in bearing lubrication performance than the friction coefficient [18]. [PERSON] et al. used acoustic emission, vibration, temperature and the CV signals to evaluate the state of the different bearing conditions and analysed the relationship between the various signals [19].