Predictive modeling of surface wear in mechanical contacts under lubricated and non-lubricated conditions

Abstract

The surface wear in mechanical contacts under running conditions is always a challenge to quantify. However, the inevitable relationship between the airborne noise and the surface wear can be used to predict the latter with good accuracy. In this paper, a predictive model has been derived to quantify surface wear by using airborne noise signals collected at a microphone. The noise was generated from a pin on disc setup on different dry and lubricated conditions. The collected signals were analyzed, and spectral features estimated from the measurements and regression models implemented in order to achieve an average wear prediction accuracy of within 1mm3.