## 5 Conclusions  
In this study, bearings of different materials were used as research objects. The boundary lubrication states of the aluminium alloy and the copper alloy bearings were determined through lubrication characteristic tests. This was followed by a 20 h boundary lubrication state bearing fatigue wear test to establish the relationship between the CV signal characteristics and the friction characteristics. The following conclusions are obtained from the research:  
1. The lubrication state of the bearing is mainly affected by the lubricating oil inlet temperature and the load. The temperature and the torque of the bearing increase with an increase in the bearing load. However, the increasing rate of the bearing temperature is lower due to the influence of thermal inertia. The load of the aluminium alloy bearing and the copper alloy bearing reached 35 MPa and 50 MPa, respectively, the lubricating oil inlet temperature was 70 \({}^{\circ}\)C, and the bearing entered the boundary lubrication state;
2. A 20 h bearing fatigue wear test conducted in the boundary lubrication state demonstrated that the amount of wear increased with an extension of the bearing wear test time, and the wear amount of the aluminium alloy bearing was greater after 10 h. The wear-reducing layer on the surface of the bearing gradually peeled off, and the wear on the bearing was intensified. The wear on the copper alloy bearing increased slowly, and its wear resistance was better than the aluminium alloy bearing;
3. There was a strong correlation between the wear of the bearing and the characteristic value of the CV signals. As the wear of the bearing increased, the characteristic value of the CV signal showed an upward trend. After 20 h, the CV characteristic value of the aluminium alloy bearing wear test increased to 195% of the initial value, whereas due to the good wear resistance of the copper alloy bearing, only slight wearing was observed, and its CV amplitude increase was relatively small. Different bearing materials responded differently with the CV signals;
4. Compared with the torque and temperature, the CV potential is more sensitive to the amount of wear, and the online monitoring of the main bearing wear could be realised by using the CV method.  
Further work that may be undertaken in this area includes: (i) completing tests of bearings in different fault states, including without lubricating oil, bearing eccentricity, etc.; (ii) deep-learning-based approaches should be used to analyse the cv signal to identify bearing fault modes, such as conventional neural network (CNN) Grey-box modelling and SVM; (iii) employing optimal algorithms, which improve the accuracy of deep-learning-based methods.  
Figure 18: The relationship between wear and thermoelectricity.  
**Author Contributions:** Conceptualisation, writing--reviewing and editing, B.W.; data curation and funding acquisition, J.Y.; formal analysis, Q.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
**Acknowledgments:** The authors are grateful to the Wuhan University of Technology for providing experimental facilities. We also appreciate all the teachers and postgraduate students who made great contributions to this project. This work was supported by the Ministry of Industry and Information Technology of the People's Republic of China via the project grant number 2019-360.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.