基于神经网络的衬垫摩擦特性预测

Friction lining is a key component of the friction hoist and its performance is directly related to the hoisting capacity, efficiency and safety. The study of the prediction of temperature rise and friction coefficient of the liner material and the hoisting wire rope at the low speed condition is of great significanceand to ensure the safety production, economic and social benefits of the mine. Simulation of mine hoist in the extreme environment of the working conditions and the tribological tests on the self-made test bed were carried out. In view of the sliding condition between friction lining and wire rope, this paper proposes a BP neural network model that can predict the temperature-rise and the friction coefficient according to the sliding velocity and pressure. Through the comparison of five groups of sample data, the neural network model can predict friction properties of K25 liner material very well.