The regulatory effects of emulsifier type and charge properties on emulsion stability and wear mechanisms were systematically investigated to the better lubrication performance, and the mapping relationship of wear types was constructed based on energy analysis of wavelet packet, then the quantitative evaluation criteria were determined to effectively identification of wear types in contact areas. The results demonstrate that the anionic emulsifier (SDBS) effectively optimized the droplet size distribution of the emulsion system to achieve the mixed lubrication. Compared with the nonionic emulsifier (Tween 80), the stability and lubrication performance of the emulsion were improved by 24.02% and 28.57%, respectively, which significantly reduced adhesion and fatigue wear in the contact interface. Furthermore, the feature signals of the friction force (energy of wavelet packet in frequency bands 1⁓4) can be used to effectively identify wear patterns in real time, thereby revealing the temporal evolution of wear under emulsion systems with different emulsifier. Therefore, it can provides not only experimental data for optimizing emulsion formulations, but also new ideas for online monitoring of wear types, which may promote the development of intelligent lubrication systems.