Design and control strategy of intelligent lubrication system for mine hoist head sheave

The sheave is an important part of a mine hoisting system. Once it fails, it will not only affect the production efficiency of coal mine, but also cause some safety risks. Poor lubrication is one of the main causes of sheave failure. In view of the low efficiency and difficulty in ensuring the uniformity and timeliness of lubrication caused by the current manual lubrication method commonly used in coal mines, this paper presented a new intelligent lubrication system for sheave bearings and implemented it on-site application. First of all, starting from sheave structure and considering the different operating modes of the sheave shaft end rolling bearing and the traveling wheel sliding bearing, it was proposed to use a combination of automatic lubrication and manual assistance to lubricate the sheave bearing. Through a real-time monitoring of the lubrication system, it realized the intelligent distribution function of single pump and multiple points with different grease amounts, and also provided the waste grease recovery function to avoid damage to bearings by waste oil. Then, an intelligent control strategy based on the identification of abnormal lubrication status was studied, and the lubrication cycle and lubrication amount of the system were adjusted based on the judgment results of the lubrication status. Subsequently, considering the impact of lubrication status identification accuracy on system operation results, an intelligent identification model of lubrication status based on the combination of Locality Preserving Projections (LPP) and Support Vector Data Description (SVDD) was constructed. In view of the problem that the number of neighboring points in the feature dimensionality reduction method will seriously affect the dimensionality reduction effect, it was proposed to use the information entropy difference between the high and low dimensional feature sets of the sample set as the objective function, and apply the particle swarm optimization algorithm to optimize the parameters of the LPP algorithm. The model was verified using on-site measured data. The results show that the proposed method can effectively identify the abnormal state of the sheave bearing, and the identification accuracy can reach 82% under on-site working conditions. Finally, an on-site application study of the intelligent lubrication system for pulley bearings was conducted in a coal mine. A single pump was used to lubricate four pulleys. The results show that the lubrication system can obtain its own operating parameters in real time and achieve remote control, ensure the continuation and uniformity of the lubrication effect, and meet the actual requirements of the mine site.