Wear performance and its online monitoring of the semimetal brake lining for automobiles

Abstract

Purpose – In order to improving the braking reliability and assuring the driving safety of automobiles, this paper aims at the wear performance and its online monitoring of its brake lining. Design/methodology/approach – The wear performance of the semimetal brake lining for automobiles was investigated on a self-made braking tester for disc brakes. Based on the experimental data, an intelligent forecasting model for the wear rate was established by the artificial neural network (ANN) technology. And by taking it as a core, an online braking wear monitoring system for automobiles was designed. Findings – It is shown that the wear rate rises obviously with the increasing of both initial braking velocity and braking pressure. By the contrast, the initial braking velocity affects the wear rate more seriously. The ANN model trained by the experimental data shows favorable capability for predicting of the wear rate. The big forecasting errors at high velocity and heavy load should be attributed to the jumping of the wear rate at this period. Based on the existed sensors and electronic control unit system of automobiles, the online braking wear monitoring system can be established easily by the ANN technology. Originality/value – A self-made braking tester for disc brakes was used to test the wear performance, which can simulate better the actual disc braking conditions than the standard pin-on-disc friction tester. An online braking wear monitoring system was designed to help improving the braking reliability and safety of automobiles.